Majors

Courses

CS 26SI: Beyond NLP: CS & Language through Text Input & Design (1) special

Where do Computer Science and Language intersect beyond NLP? In this class, we explore their overlaps through text entry and design. On the text-entry side, we will learn about the writing systems of the world and their encodings (there is so much more beyond the Latin alphabet!), how keyboards work and why they are designed this way, how autocorrect / predictive typing and voice / handwriting text input function, and accessibility of text input. On the design side, we will learn about typography & typeface design, and l10n / i18n. This class will feature many case studies and a few guest speakers!

CS 29N: Computational Decision Making (3) special

Although we make decisions every day, many people base their decisions on initial reactions or 'gut' feelings. There are, however, powerful frameworks for making decisions more effectively based on computationally analyzing the choices available and their possible outcomes. In this course we give an introduction to some of these frameworks, including utility theory, decision analysis, and game theory. We also discuss why people sometimes make seemingly reasonable, yet irrational, decisions. We begin the class by presenting some of the basics of probability theory, which serves as the main mathematical foundation for the decision making frameworks we will subsequently present. Although we provide a mathematical/computational basis for the decision making frameworks we examine, we also seek to give intuitive (and sometimes counterintuitive) explanations for actual decision making behavior through in-class demonstrations. No prior experience with probability theory is needed (we'll cover what you need to know in class), but students should be comfortable with mathematical manipulation at the level of MATH 20 or MATH 41.

CS 31N: Counterfactuals: The Science of What Ifs? (3) special

How might the past have changed if different decisions were made? This question has captured the fascination of people for hundreds of years. By precisely asking, and answering such questions of counterfactual inference, we have the opportunity to both understand the impact of past decisions (has climate change worsened economic inequality?) and inform future choices (can we use historical electronic medical records data about decision made and outcomes, to create better protocols to enhance patient health?). In this course I will introduce some of the most common quantitative approaches to counterfactual reasoning, as well as give a wide sampling of some of the many important problems and questions that can be addressed through the lens of counterfactual reasoning, including in climate change, healthcare and economics. No prior experience with counterfactual or 'what if' reasoning, nor probability, is required.

CS 40: Cloud Infrastructure and Scalable Application Deployment (3) sys

Trying to launch your next viral programming project and anticipating substantial user growth? This course will help you learn to implement your ideas in the cloud in a scalable, cost-effective manner. Topics will include cloud AI/ML pipelines, virtual machines, containers, basic networking, expressing infrastructure as code (IaC), data management, security and observability, and continuous integration and deployment (CI/CD). Through hands-on learning and practical examples, you'll learn to effectively deploy and manage cloud infrastructure. There is no out-of-pocket cost associated with this class and cloud credits will be provided for all students.

CS 41: Hap.py Code: The Python Programming Language (2) intro

This course is about the fundamentals and contemporary usage of the Python programming language. The primary focus is on developing best practices in writing Python and exploring the extensible and unique parts of the Python language. Topics include: Pythonic conventions, data structures such as list comprehensions, anonymous functions, iterables, powerful built-ins (e.g. map, filter, zip), and Python libraries. For the last few weeks, students will work with course staff to develop their own significant Python project.

CS 44N: Great Ideas in Graphics (3) graphics

A hands-on interactive and fun exploration of great ideas from computer graphics. Motivated by graphics concepts, mathematical foundations and computer algorithms, students will explore an eccentric selection of 'great ideas' through short weekly programming projects. Project topics will be selected from a diverse array of computer graphics concepts and historical elements.

CS 45: Software Tools Every Programmer Should Know (2) sys

Classes teach you all about advanced topics within CS, from operating systems to machine learning, but there's one critical subject that's rarely covered, and is instead left to students to figure out on their own: proficiency with their tools. This course will teach you how to master the key tools necessary for being a successful computer scientist, such as the command line, version control systems, debuggers and linters, and many more. In addition, we will cover other key topics that are left out of standard CS classes, but that are essential to being a proficient computer scientist, including: security and cryptography, containers and virtual machines, and cloud computing.

CS 46: Working with Data: Delights and Doubts (3) special

The use of data to drive decisions and discoveries has increased dramatically over the past two decades, thanks to prevalent data collection, cheaper storage, faster computers, and sophisticated algorithms. This introductory seminar has three components: (1) Hands-on instruction in tools and techniques for working with data, from spreadsheets to data visualization systems to machine learning packages. This material is designed for students with little or no computer programming or data science experience. (2) A quarter-long 'quantified self' project where students identify a set of questions about themselves or their surroundings, collect data to answer the questions, and analyze and visualize the collected data. (3) A set of guest speakers, including some who focus on the 'doubts' of collecting and exploiting data, such as questions of ethics, bias, and privacy. In addition to the course project, students will complete short assignments to practice the learned tools and techniques, and will be expected to do some readings in advance of each guest speaker and engage in thoughtful discussion.

CS 47: Cross-Platform Mobile Development (2) sys

The fundamentals of cross-platform mobile application development using the React Native framework (RN). The Primary focus is on enabling students to build apps for both iOS and Android using RN. Students will explore the unique aspects that made RN a primary tool for mobile development within Facebook, Instagram, Walmart, Tesla, and UberEats, SpaceX, Coinbase and many more.

CS 47N: Datathletics: Diving into Data Analytics and Stanford Sports (3) special

Sophisticated data collection and analysis are now key to program success across many sports: Nearly all professional and national-level teams employ data scientists, and 'datathletics' is becoming prevalent in college sports as well. This immersive seminar combines extensive hands-on data analytics with a first-hand peek into Stanford athletics. Class meetings roughly alternate between: (1) instruction in a variety of tools and techniques for analyzing and visualizing data; and (2) guest lectures by Stanford athletics coaches explaining how data is or could be used in their sport. Through regular problem sets, students bring each week's tools to bear on data related to the week's sport. One goal of the class is empowering students to perform compelling data analytics by mastering tools across a wide spectrum, including spreadsheets, the Tableau system for data preparation and visualization, Jupyter notebooks, relational databases and SQL, Python and many of its data-specific packages including Pandas, and machine learning. On the sports side, while the Stanford coaches may touch on many aspects of data collection and analysis, the main focus of this course is on using data for strategic decision-making rather than optimizing individual human performance.

CS 49N: Using Bits to Control Atoms (3) sys

This is a crash course in how to use a stripped-down computer system about the size of a credit card (the rasberry pi computer) to control as many different sensors as we can implement in ten weeks, including LEDs, motion sensors, light controllers, and accelerometers. The ability to fearlessly grab a set of hardware devices, examine the data sheet to see how to use it, and stitch them together using simple code is a secret weapon that software-only people lack, and allows you to build many interesting gadgets. We will start with a 'bare metal' system --- no operating system, no support --- and teach you how to read device data sheets describing sensors and write the minimal code needed to control them (including how to debug when things go wrong, as they always do). This course differs from most in that it is deliberately mostly about what and why rather than how --- our hope is that the things you are able at the end will inspire you to follow the rest of the CS curriculum to understand better how things you've used work.

CS 51: CS + Social Good Studio: Designing Social Impact Projects (2) intro

Get real-world experience researching and developing your own social impact project! Students work in small teams to develop high-impact projects around problem domains provided by partner organizations, under the guidance and support of design/technical coaches from industry and non-profit domain experts. Main class components are workshops, community discussions, guest speakers and mentorship. Studio provides an outlet for students to create social change through CS while engaging in the full product development cycle on real-world projects. The class culminates in a showcase where students share their project ideas and Minimum Viable Product prototypes with stakeholders and the public. Application required; please see CS 51.stanford.edu for more information.

CS 52: CS + Social Good Studio: Implementing Social Good Projects (2) intro

Continuation of CS 51 (CS + Social Good Studio). Teams enter the quarter having completed and tested a minimal viable product (MVP) with a well-defined target user, and a community partner. Students will learn to apply scalable technical frameworks, methods to measure social impact, tools for deployment, user acquisition techniques and growth/exit strategies. The purpose of the class is to facilitate students to build a sustainable infrastructure around their product idea. CS 52 will host mentors, guest speakers and industry experts for various workshops and coaching-sessions. The class culminates in a showcase where students share their projects with stakeholders and the public.

CS 53N: How Can Generative AI Help Us Learn? (3) ai

This seminar course will explore the science behind generative AI, the likely future of tools such as DALL-E, ChatGPT, GPT-4, and Bard, and the implications for education, both in and outside of structured school environments. Students in the course will work in teams to each become experts in some aspect of AI and in some way that generative AI could create a new future for education. The background for this course is the public release of ChatGPT, which created new awareness of the potential power of AI to dramatically change our lives. In considering the possible implications for education, ChatGPT has sparked dreams of automated personal tutors, customizable teaching assistance, AI-led collaborative learning, and revolutions in assessment. In addition to optimistic projections, there are clear and significant risks. For example, will AI-assisted learning be culturally appropriate and equally available to all? Can it increase opportunity for underprivileged learners worldwide, or will it accentuate privilege and privileged views? Will it help us learn faster, or distract us from thinking deeply about difficult problems ourselves? As experienced student learners, members of the class will be able to draw on their own educational history and design learning approaches that could change the future of their education and others in college or at other stages of their lives.

CS 56N: Great Discoveries and Inventions in Computing (3)

This seminar will explore some of both the great discoveries that underlie computer science and the inventions that have produced the remarkable advances in computing technology. Key questions we will explore include: What is computable? How can information be securely communicated? How do computers fundamentally work? What makes computers fast? Our exploration will look both at the principles behind the discoveries and inventions, as well as the history and the people involved in those events. Some exposure to programming is required.

CS 57N: Randomness: Computational and Philosophical Approaches (PHIL 3N) (3)

Is it ever reasonable to make a decision randomly? For example, would you ever let an important choice depend on the flip of a coin? Can randomness help us answer difficult questions more accurately or more efficiently? What is randomness anyway? Can an object be random? Are there genuinely random processes in the world, and if so, how can we tell? In this seminar, we will explore these questions through the lenses of philosophy and computation. By the end of the quarter students should have an appreciation of the many roles that randomness plays in both humanities and sciences, as well as a grasp of some of the key analytical tools used to study the concept. The course will be self-contained, and no prior experience with randomness/probability is necessary.

CS 59SI: Quantum Computing: Open-Source Project Experience (2) special

This course focuses on giving quantum software engineering industry experience with open-source projects proposed by frontier quantum computing and quantum device corporate partners. Quantum computing and quantum information industry sponsors submit open-source projects for students or teams of students to build and create solutions throughout the quarter with mentorship from the company. Gain experience with quantum mechanics, quantum computing, and real-world software development.

CS 64: Computation for Puzzles and Games (1) ai

How can we apply computer science to better understand (and have even more fun with) games and puzzles? What can we do when a game is too complex to analyze exhaustively, or when no efficient algorithms exist to solve a logic puzzle? This sampler course will whet your appetite for CS theory and AI as we apply those lenses to both classics (e.g., chess, Scrabble, the Rubik's cube, the Lights Out puzzle) and modern favorites (e.g., Sudoku, Kakuro, Esports, and tool-assisted speedruns). Each week, we will have one lecture and one optional hands-on puzzle/problem solving session, culminating in an (optional) on-foot puzzle hunt around campus. Material of varying technical complexity will be presented, and although some experience with programming and CS theory will be helpful, the course is open to all.

CS 80E: Dissecting The Modern Computer (2) sys

In this course, students will be given a high-level, accessible introduction to computer architecture through the use of the RISC-V ISA. Through a series of interactive units, students will learn about the inner-workings of computers, from the execution of our programs all the way down to the hardware that runs them. Topics include simple digital circuits, assembly, simple processors, memory systems (Cache, DRAM, Disk), and bonus topics like GPU's. After completing this class, students should have a newfound appreciation for how incredible computational technology is, as well as direction to fantastic classes that delve into some of these topics in more detail, like CS 149, EE 108, and EE 180.

CS 80Q: Race and Gender in Silicon Valley (3) impact

Join us as we go behind the scenes of some of the big headlines about trouble in Silicon Valley. We'll start with the basic questions like who decides who gets to see themselves as 'a computer person,' and how do early childhood and educational experiences shape our perceptions of our relationship to technology? Then we'll see how those questions are fundamental to a wide variety of recent events from #metoo in tech companies, to the ways the under-representation of women and people of color in tech companies impacts the kinds of products that Silicon Valley brings to market. We'll see how data and the coming age of AI raise the stakes on these questions of identity and technology. How can we ensure that AI technology will help reduce bias in human decision-making in areas from marketing to criminal justice, rather than amplify it?

CS 83N: Playback Theater (3)

Playback combines elements of theater, community work and storytelling. In a playback show, a group of actors and musicians create an improvised performance based on the audience's personal stories. A playback show brings about a powerful listening and sharing experience. During the course, we will tell, listen, play together, and train in playback techniques. We will write diaries to process our experience in the context of education and research. The course is aimed to strengthen listening abilities, creativity and the collaborative spirit, all integral parts of doing great science. In playback, as in research, we are always moving together, from the known, to the unknown, and back. There is limited enrollment for this class. Application is required.

CS 91: Digital Canvas: An Introduction to UI/UX Design (2) humans

This course is focused on the application of UX/UI design concepts to actual user interfaces: the creation of wireframes, high-fidelity mockups, and clickable prototypes. We will be focusing on what makes a good or bad user interface, effective design techniques, and how to employ these techniques using Figma and Marvel to make realistic prototypes. This course is ideal for anyone with little to no visual design experience who would like to build their skill set in UI/UX for app or web design. It would also be ideal for anyone with experience in front or back-end web development or human-computer interaction that would want to sharpen their visual design and analysis skills for UI/UX.

CS 91SI: Digital Canvas: An Introduction to UI/UX Design (2) humans

In this course, students learn digital design in a low-stress environment. We will teach the essential concepts of UI/UX design and create actual user interfaces in a project-based format. By the end of the class, students will have experience in creating handoff-ready interactive high-fidelity mockups for a realistic product feature. This course covers what makes a good or bad interface, effective design techniques from the ground up, and how to execute on design principles using the tool Figma. Limited enrollment - admission determined by short application due 11:59 PM on March 23: https://forms.gle/knsLbRwt7th4HHsb7 . No required prerequisites. Recommended: some prior experience in product design, human-computer interaction, or front-end engineering

CS 100ACE: Problem-solving Lab for CS 106A (1) intro

Additional problem solving practice for the introductory CS course CS 106A. Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material. Limited enrollment, permission of instructor required. Concurrent enrollment in CS 106A required.

CS 103: Mathematical Foundations of Computing (35) theory

What are the theoretical limits of computing power? What problems can be solved with computers? Which ones cannot? And how can we reason about the answers to these questions with mathematical certainty? This course explores the answers to these questions and serves as an introduction to discrete mathematics, computability theory, and complexity theory. At the completion of the course, students will feel comfortable writing mathematical proofs, reasoning about discrete structures, reading and writing statements in first-order logic, and working with mathematical models of computing devices. Throughout the course, students will gain exposure to some of the most exciting mathematical and philosophical ideas of the late nineteenth and twentieth centuries. Specific topics covered include formal mathematical proofwriting, propositional and first-order logic, set theory, binary relations, functions (injections, surjections, and bijections), cardinality, basic graph theory, the pigeonhole principle, mathematical induction, finite automata, regular expressions, the Myhill-Nerode theorem, context-free grammars, Turing machines, decidable and recognizable languages, self-reference and undecidability, verifiers, and the P versus NP question. Students with significant proofwriting experience are encouraged to instead take CS 154. Students interested in extra practice and support with the course are encouraged to concurrently enroll in CS 103A.

CS 103ACE: Mathematical Problem-solving Strategies (1) theory

Problem solving strategies and techniques in discrete mathematics and computer science. Additional problem solving practice for CS 103. In-class participation required.

CS 104: Introduction to Essential Software Systems and Tools (3) sys

Concepts that are prerequisites to many different CS classes, such as version control, debugging, and basic cryptography and networking, are either left for students to figure out on their own or are taught in 'crash course' form on-the-fly during other, unrelated classes. We propose to develop a course that will teach students the skills necessary to be successful computer scientists, such as the command line, source code management and debugging, security and cryptography, containers and virtual machines, and cloud computing. In this course, students will both become proficient with practical tools and develop a deeper, intuitive understanding of the involved software systems and computer science concepts. With this deeper understanding, students can leverage critical thinking skills to intelligently and efficiently configure and troubleshoot software systems, assess the security and efficiency of particular tool usages, and synthesize new automation pipelines that integrate multiple tools. To summarize, instead of having just a cursory understanding of how to use these tools, students will learn how to most effectively use these tools to become proficient programmers and computer scientists. In addition, this course can provide a gentle introduction to potentially challenging computer science concepts (e.g., networking) that become a focus in subsequent courses and also help motivate some of the tool usages they will see later in the degree program.

CS 105: Introduction to Computers (35) intro

For non-technical majors. What computers are and how they work. Practical experience in development of websites and an introduction to programming. A survey of Internet technology and the basics of computer hardware. Students in technical fields and students looking to acquire programming skills should take 106A or 106X. Students with prior computer science experience at the level of 106 or above require consent of instructor.

CS 106A: Programming Methodology (35) intro

Introduction to the engineering of computer applications emphasizing modern software engineering principles: program design, decomposition, encapsulation, abstraction, and testing. Emphasis is on good programming style and the built-in facilities of respective languages. Uses the Python programming language. No prior programming experience required.

CS 106AX: Programming Methodologies in JavaScript and Python (Accelerated) (35) intro

Introduction to the engineering of computer applications emphasizing modern software engineering principles: object-oriented design, decomposition, encapsulation, abstraction, and testing. This course targets an audience with prior programming experience, and that prior experience is leveraged so material can be covered in greater depth.

CS 106B: Programming Abstractions (35) intro

Abstraction and its relation to programming. Software engineering principles of data abstraction and modularity. Object-oriented programming, fundamental data structures (such as stacks, queues, sets) and data-directed design. Recursion and recursive data structures (linked lists, trees, graphs). Introduction to time and space complexity analysis. Uses the programming language C++ covering its basic facilities.

CS 106E: Exploration of Computing (3) intro

This course, designed for the non-computer scientist, will provide students with a solid foundation in the concepts and terminology behind computers, the Internet, and software development. It will give you better understanding and insight when working with technology. It will be particularly useful to future managers and PMs who will work with or who will lead programmers and other tech workers. But it will be useful to anyone who wants a better understanding of tech concepts and terms. We'll start by covering the foundations of Computer Hardware, the CPU, Operating Systems, Computer Networks, and the Web. We will then use our foundation to explore a variety of tech-related topics including Computer Security (how computers are attacked and defensive measures that can be taken); Cloud Computing, Artificial Intelligence, Software Development, Human-Computer Interaction, and Computer Theory.

CS 106L: Standard C++ Programming Laboratory (1) intro

This class explores features of the C++ programming language beyond what's covered in CS 106B. Topics include core C++ language features (e.g. const-correctness, operator overloading, templates, move semantics, and lambda expressions) and standard libraries (e.g. containers, algorithms, and smart pointers).

CS 106M: Enrichment Adventures in Programming Abstractions (1) intro

This enrichment add-on is a companion course to CS 106B to explore additional topics and go into further depth. Specific topics to be announced per-quarter; past topics have included search engines, pattern recognition, data compression/encryption, error correction, digital signatures, and numerical recipes.

CS 106S: Coding for Social Good (1) intro

Survey course on applications of fundamental computer science concepts from CS 106B to problems in the social good space (such as health, trust & safety, government, security, education, and environment). Each week consists of in-class activities designed and delivered by student instructors. Introduces students to JavaScript and the basics of web development. Some of the topics we will cover include mental health chatbots, tumor classification with basic machine learning, sentiment analysis of tweets on refugees, the basics of open source software, and principles of cybersecurity.

CS 107: Computer Organization and Systems (35) sys

Introduction to the fundamental concepts of computer systems. Explores how computer systems execute programs and manipulate data, working from the C programming language down to the microprocessor. Topics covered include: the C programming language, data representation, machine-level code, computer arithmetic, elements of code compilation, memory organization and management, and performance evaluation and optimization.

CS 107ACE: Problem-solving Lab for CS 107 (1) sys

Additional problem solving practice for the introductory CS course CS 107. Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material.

CS 107E: Computer Systems from the Ground Up (35) sys

Introduction to the fundamental concepts of computer systems through bare metal programming on the Raspberry Pi. Explores how five concepts come together in computer systems: hardware, architecture, assembly code, the C language, and software development tools. Students do all programming with a Raspberry Pi kit and several add-ons (LEDs, buttons). Topics covered include: the C programming language, data representation, machine-level code, computer arithmetic, compilation, memory organization and management, debugging, hardware, and I/O.

CS 108: Object-Oriented Systems Design (34) intro

Software design and construction in the context of large OOP libraries. Taught in Java. Topics: OOP design, design patterns, testing, graphical user interface (GUI) OOP libraries, software engineering strategies, approaches to programming in teams.

CS 109: Introduction to Probability for Computer Scientists (35) math

Topics include: counting and combinatorics, random variables, conditional probability, independence, distributions, expectation, point estimation, and limit theorems. Applications of probability in computer science including machine learning and the use of probability in the analysis of algorithms.

CS 109ACE: Problem-solving Lab for CS 109 (1) math

Additional problem solving practice for the introductory CS course CS 109. Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material. Enrollment limited to 30 students, permission of instructor required. Concurrent enrollment in CS 109 required.

CS 110: Principles of Computer Systems (35) sys

Principles and practice of engineering of computer software and hardware systems. Topics include: techniques for controlling complexity; strong modularity using client-server design, virtual memory, and threads; networks; atomicity and coordination of parallel activities.

CS 110A: Problem Solving Lab for CS 110 (1) sys

Additional design and implementation problems to complement the material taught in CS 110. In-class participation is required.

CS 110L: Safety in Systems Programming (2) sys

Supplemental lab to CS 110. Explores how program analysis tools can find common bugs in programs and demonstrates how we can use the Rust programming language to build robust systems software. Course is project-based and will examine additional topics in concurrency and networking through the lens of Rust.

CS 111: Operating Systems Principles (35) sys

Explores operating system concepts including concurrency, synchronization, scheduling, processes, virtual memory, I/O, file systems, and protection. Available as a substitute for CS 110 that fulfills any requirement satisfied by CS 110.

CS 111ACE: Problem Solving Lab for CS 111 (1) sys

Additional design and implementation problems to complement the material taught in CS 111. In-class participation is required.

CS 112: Operating systems kernel implementation project (3) sys

Students will learn the details of how operating systems work through four implementation projects in the Pintos operating system. The projects center around threads, processes, virtual memory, and file systems.

CS 114: Selected Reading of Computer Science Research (3) special

Detailed reading of 5-10 research publications in computer science. For undergraduates, the course is an introduction to advanced foundational concepts within a field as well as an in-depth look at detailed research. For graduate students, the course focuses on historical reading as well as an opportunity to discuss the strengths and weaknesses of the work. Both groups of students discuss historical context, how ideas succeeded or did not and why, and how they manifest in modern technology. The discussion of each piece of work includes a guest lecture by one of its authors.

CS 120: Introduction to AI Safety (STS 10) (3) ai

As we delegate more to artificial intelligence (AI) and integrate AI more in societal decision-making processes, we must find answers to how we can ensure AI systems are safe, follow ethical principles, and align with the creator's intent. Increasingly, many AI experts across academia and industry believe there is an urgent need for both technical and societal progress across AI alignment, ethics, and governance to understand and mitigate risks from increasingly capable AI systems and ensure that their contributions benefit society as a whole. Intro to AI Safety explores these questions in lectures with targeted readings, weekly quizzes, and group discussions. We are looking at the capabilities and limitations of current and future AI systems to understand why it is hard to ensure the reliability of existing AI systems. We will cover ongoing research efforts that tackle these questions, ranging from studies in reinforcement learning and computer vision to natural language processing. We will study work in interpretability, robustness, and governance of AI systems - to name a few. Basic knowledge about machine learning helps but is not required.

CS 123: A Hands-On Introduction to Building AI-Enabled Robots (3) ai

This course offers a hands-on introduction to AI-powered robotics. Unlike most introductory robotics courses, students will learn essential robotics concepts by constructing a quadruped robot from scratch and training it to perform real-world tasks. The course covers a broad range of topics critical to robot learning, including motor control, forward and inverse kinematics, system identification, simulation, and reinforcement learning. Through weekly labs, students will construct a pair of tele-operated robot arms with haptic feedback, program a robot arm to learn self-movement, and ultimately create and program an agile robot quadruped named Pupper. In the final four weeks, students will undertake an open-ended project using Pupper as a platform, such as instructing it to walk using reinforcement learning, developing a vision system to allow Pupper to play fetch, or redesigning the hardware to enhance the robot's agility. Note: CS 123 strives to achieve a balanced distribution of seniority across the undergrad student body. Within each seniority group, enrollment of students will follow a first-come-first-served approach.

CS 124: From Languages to Information (LINGUIST 180, LINGUIST 280) (34) ai

Extracting meaning, information, and structure from human language text, speech, web pages, social networks. Introducing methods (regex, edit distance, naive Bayes, logistic regression, neural embeddings, inverted indices, collaborative filtering, PageRank), applications (chatbots, sentiment analysis, information retrieval, question answering, text classification, social networks, recommender systems), and ethical issues in both.

CS 125: Data: Algorithms, Tools, Policy, and Society (POLISCI 156) (3) impact

A broad multidisciplinary examination of the use and impacts of data, including fundamental principles and algorithms, tools for data analysis, visualization, and machine learning, policy issues, and societal considerations. Specific topics include: data provenance (where data comes from and how it's processed), the role and value of data in analytics and decision-making, data and algorithmic fairness, data privacy, the concentration of data as power, and issues of data governance and regulation, including transparency and due process. In addition to case studies, conceptual frameworks, theoretical underpinnings, and algorithms, the course provides practical experience through hands-on work where students use tools to explore issues from class on real data.

CS 129: Applied Machine Learning (34) ai

You will learn to implement and apply machine learning algorithms. This course emphasizes practical skills, and focuses on giving you skills to make these algorithms work. You will learn about commonly used learning techniques including supervised learning algorithms (logistic regression, linear regression, SVM, neural networks/deep learning), unsupervised learning algorithms (k-means), as well as learn about specific applications such as anomaly detection and building recommender systems. This class is taught in the flipped-classroom format. You will watch videos and complete in-depth programming assignments and online quizzes at home, then come to class for discussion sections. This class will culminate in an open-ended final project, which the teaching team will help you on.

CS 129X: Human Centered NLP (CS 329X) (34) ai

Recent advances in natural language processing (NLP), especially around large pretrained models, have enabled extensive successful applications. However, there are growing concerns about the negative aspects of NLP systems, such as biases and a lack of input from users. This course gives an overview of human-centered techniques and applications for NLP, ranging from human-centered design thinking to human-in-the-loop algorithms, fairness, and accessibility. Along the way, we will cover machine-learning techniques which are especially relevant to NLP and to human experiences.

CS 131: Computer Vision: Foundations and Applications (34) ai

Computer Vision technologies are transforming automotive, healthcare, manufacturing, agriculture and many other sections. Today, household robots can navigate spaces and perform duties, search engines can index billions of images and videos, algorithms can diagnose medical images for diseases, and smart cars can see and drive safely. Lying in the heart of these modern AI applications are computer vision technologies that can perceive, understand, and reconstruct the complex visual world. This course is designed for students who are interested in learning about the fundamental principles and important applications of Computer Vision. This course will introduce a number of fundamental concepts in image processing and expose students to a number of real-world applications. It will guide students through a series of projects to implement cutting-edge algorithms. There will be optional discussion sections on Fridays.

CS 137A: Principles of Robot Autonomy I (AA 174A, EE 160A) (34) ai

Basic principles for endowing mobile autonomous robots with perception, planning, and decision-making capabilities. Algorithmic approaches for robot perception, localization, and simultaneous localization and mapping; control of non-linear systems, learning-based control, and robot motion planning; introduction to methodologies for reasoning under uncertainty, e.g., (partially observable) Markov decision processes. Extensive use of the Robot Operating System (ROS) for demonstrations and hands-on activities.

CS 139: Human-Centered AI (3) ai

Artificial Intelligence technology can and must be guided by human concerns. The course examines how mental models and user models of AI systems are formed, and how that leads to user expectations. This informs a set of design guidelines for building AI systems that are trustworthy, understandable, fair, and beneficial. The course covers the impact of AI systems on the economy and everyday life, and ethical issues of collecting data and running systems, including respect for persons, beneficence, fairness and justice.

CS 140: Operating Systems and Systems Programming (35) sys

Covers key concepts in computer systems through the lens of operating system design and implementation. Topics include threads, scheduling, processes, virtual memory, synchronization, multi-core architectures, memory consistency, hardware atomics, memory allocators, linking, I/O, file systems, and virtual machines. Concepts are reinforced with four kernel programming projects in the Pintos operating system. This class may be taken as an accelerated single-class alternative to the CS 111, CS 112 sequence; conversely, the class should not be taken by students who have already taken CS 111 or CS 112.

CS 140E: Operating systems design and implementation (34) sys

Students will implement a simple, clean operating system (virtual memory, processes, file system) in the C programming language, on a rasberry pi computer and use the result to run a variety of devices and implement a final project. All hardware is supplied by the instructor, and no previous experience with operating systems, raspberry pi, or embedded programming is required.

CS 142: Web Applications (3) sys

Concepts and techniques used in constructing interactive web applications. Browser-side web facilities such as HTML, cascading stylesheets, the document object model, and JavaScript frameworks and Server-side technologies such as server-side JavaScript, sessions, and object-oriented databases. Issues in web security and application scalability. New models of web application deployment.

CS 143: Compilers (34) pls

Principles and practices for design and implementation of compilers and interpreters. Topics: lexical analysis; parsing theory; symbol tables; type systems; scope; semantic analysis; intermediate representations; runtime environments; code generation; and basic program analysis and optimization. Students construct a compiler for a simple object-oriented language during course programming projects.

CS 144: Introduction to Computer Networking (34) sys

Principles and practice. Structure and components of computer networks, with focus on the Internet. Packet switching, layering, and routing. Transport and TCP: reliable delivery over an unreliable network, flow control, congestion control. Network names, addresses and ethernet switching. Includes significant programming component in C/C++; students build portions of the internet TCP/IP software.

CS 145: Data Management and Data Systems (34) sys

Introduction to the use, design, and implementation of database and data-intensive systems, including data models; schema design; data storage; query processing, query optimization, and cost estimation; concurrency control, transactions, and failure recovery; distributed and parallel execution; semi-structured databases; and data system support for advanced analytics and machine learning.

CS 147: Introduction to Human-Computer Interaction Design (35) humans

Introduces fundamental methods and principles for designing, implementing, and evaluating user interfaces. Topics: user-centered design, rapid prototyping, experimentation, direct manipulation, cognitive principles, visual design, social software, software tools. Learn by doing: work with a team on a quarter-long design project, supported by lectures, readings, and studios.

CS 147L: Cross-platform Mobile App Development (3) sys

The fundamentals of cross-platform mobile application development with a focus on the React Native framework (RN). Primary focus on developing best practices in creating apps for both iOS and Android by using Javascript and existing web + mobile development paradigms. Students will explore the unique aspects that made RN a primary tool for mobile development within Facebook, Instagram, Airbnb, Walmart, Tesla, and UberEats. Skills developed over the course will be consolidated by the completion of a final project.

CS 148: Introduction to Computer Graphics and Imaging (34) graphics

This is the introductory prerequisite course in the computer graphics sequence which introduces students to the technical concepts behind creating synthetic computer generated images. The beginning of the course focuses on using Blender to create visual imagery, as well as an understanding of the underlying mathematical concepts including triangles, normals, interpolation, texture mapping, bump mapping, etc. Then we move on to a more fundamental understanding of light and color, as well as how it impacts computer displays and printers. From this we discuss more thoroughly how light interacts with the environment, and we construct engineering models such as the BRDF and discuss various simplifications into more basic lighting and shading models. Finally, we discuss ray tracing technology for creating virtual images, while drawing parallels between ray tracers and real world cameras in order to illustrate various concepts. Anti-aliasing and acceleration structures are also discussed. The final class project consists of building out a ray tracer to create a visually compelling image. Starter codes and code bits will be provided here and there to aid in development, but this class focuses on what you can do with the code as opposed to what the code itself looks like. Therefore grading is weighted towards in person 'demos' of the code in action - creativity and the production of impressive visual imagery are highly encouraged.

CS 149: Parallel Computing (34) sys

This course is an introduction to parallelism and parallel programming. Most new computer architectures are parallel; programming these machines requires knowledge of the basic issues of and techniques for writing parallel software. Topics: varieties of parallelism in current hardware (e.g., fast networks, multicore, accelerators such as GPUs, vector instruction sets), importance of locality, implicit vs. explicit parallelism, shared vs. non-shared memory, synchronization mechanisms (locking, atomicity, transactions, barriers), and parallel programming models (threads, data parallel/streaming, MapReduce, Apache Spark, SPMD, message passing, SIMT, transactions, and nested parallelism). Significant parallel programming assignments will be given as homework. The course is open to students who have completed the introductory CS course sequence through 111.

CS 151: Logic Programming (3) pls

Logic Programming is a style of programming based on symbolic logic. In writing a logic program, the programmer describes the application area of the program (as a set of logical sentences) without reference to the internal data structures or operations of the system executing the program. In this regard, a logic program is more of a specification than an implementation; and logic programs are often called runnable specifications. This course introduces basic logic programming theory, current technology, and examples of common applications, notably deductive databases, logical spreadsheets, enterprise management, computational law, and game playing. Work in the course takes the form of readings and exercises, weekly programming assignments, and a term-long project.

CS 152: Trust and Safety (COMM 122, INTLPOL 267) (3) sys

Trust and Safety is an emerging field of professional and academic effort to build technologies that allow people to positively use the internet while being safe from harm. This course provides an introduction to the ways online services are abused to cause real human harm and the potential social, operational, product, legal and engineering responses. Students will learn about fraud, account takeovers, the use of social media by terrorists, misinformation, child exploitation, harassment, bullying and self-harm. This will include studying both the technical and sociological roots of these harms and the ways various online providers have responded. The class is taught by a practitioner, a professor of communication, a political scientist, and supplemented by guest lecturers from tech companies and nonprofits. Cross-disciplinary teams of students will spend the quarter building a technical and policy solution to a real trust and safety challenge, which will include the application of AI technologies to detecting and stopping abuse. For those taking this course for CS credit, the prerequisite is CS 106B or equivalent programming experience and this course fulfills the Technology in Society requirement. Content note: This class will cover real-world harmful behavior and expose students to potentially upsetting material.

CS 153: Applied Security at Scale (3) sys

This course is designed to help students understand the unique challenges of solving security problems at scale, and is taught by senior technology leaders from companies tackling hardware and software security for hundreds of millions of people. The course is split into six parts covering major themes: Basics, Confidential Computing, Privacy, Trust, Safety and Real World. The format of the class will include guest lectures from experts in each theme, covering a blend of both theory and real world scenarios.

CS 154: Introduction to the Theory of Computation (34) theory

This course provides a mathematical introduction to the following questions: What is computation? Given a computational model, what problems can we hope to solve in principle with this model? Besides those solvable in principle, what problems can we hope to efficiently solve? In many cases we can give completely rigorous answers; in other cases, these questions have become major open problems in computer science and mathematics. By the end of this course, students will be able to classify computational problems in terms of their computational complexity (Is the problem regular? Not regular? Decidable? Recognizable? Neither? Solvable in P? NP-complete? PSPACE-complete?, etc.). Students will gain a deeper appreciation for some of the fundamental issues in computing that are independent of trends of technology, such as the Church-Turing Thesis and the P versus NP problem.

CS 155: Computer and Network Security (3) sys

For juniors, seniors, and first-year graduate students. Principles of computer systems security. Attack techniques and how to defend against them. Topics include: network attacks and defenses, operating system security, application security (web, apps, databases), malware, privacy, and security for mobile devices. Course projects focus on building reliable software.

CS 157: Computational Logic (3) theory

Rigorous introduction to Symbolic Logic from a computational perspective. Encoding information in the form of logical sentences. Reasoning with information in this form. Overview of logic technology and its applications - in mathematics, science, engineering, business, law, and so forth. Topics include the syntax and semantics of Propositional Logic, Relational Logic, and Herbrand Logic, validity, contingency, unsatisfiability, logical equivalence, entailment, consistency, natural deduction (Fitch), mathematical induction, resolution, compactness, soundness, completeness.

CS 161: Design and Analysis of Algorithms (35) algs

Worst and average case analysis. Recurrences and asymptotics. Efficient algorithms for sorting, searching, and selection. Data structures: binary search trees, heaps, hash tables. Algorithm design techniques: divide-and-conquer, dynamic programming, greedy algorithms, amortized analysis, randomization. Algorithms for fundamental graph problems: minimum-cost spanning tree, connected components, topological sort, and shortest paths. Possible additional topics: network flow, string searching.

CS 161ACE: Problem-Solving Lab for CS 161 (1) algs

Additional problem solving practice for CS 161. Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material. Concurrent enrollment in CS 161 required. Limited enrollment, permission of instructor, and application required.

CS 163: The Practice of Theory Research (3) theory

(Previously numbered CS 353). Introduction to research in the Theory of Computing, with an emphasis on research methods (the practice of research), rather than on any particular body of knowledge. The students will participate in a highly structured research project: starting from reading research papers from a critical point of view and conducting bibliography searches, through suggesting new research directions, identifying relevant technical areas, and finally producing and communicating new insights. The course will accompany the projects with basic insights on the main ingredients of research. Research experience is not required, but basic theory knowledge and mathematical maturity are expected. The target participants are advanced undergrads as well as MS students with interest in CS theory.

CS 166: Data Structures (34) algs

This course is a deep dive into the design, analysis, implementation, and theory of data structures. Over the course of the quarter, we'll explore fundamental techniques in data structure design (isometries, amortization, randomization, etc.) and explore perspectives and intuitions useful for developing new data structures. We'll do so by surveying classic data structures like Fibonacci heaps and suffix trees, as well as more modern data structures like count-min sketches and range minimum queries. By the time we've finished, we'll have seen some truly beautiful strategies for solving problems efficiently.

CS 168: The Modern Algorithmic Toolbox (34) algs

This course will provide a rigorous and hands-on introduction to the central ideas and algorithms that constitute the core of the modern algorithms toolkit. Emphasis will be on understanding the high-level theoretical intuitions and principles underlying the algorithms we discuss, as well as developing a concrete understanding of when and how to implement and apply the algorithms. The course will be structured as a sequence of one-week investigations; each week will introduce one algorithmic idea, and discuss the motivation, theoretical underpinning, and practical applications of that algorithmic idea. Each topic will be accompanied by a mini-project in which students will be guided through a practical application of the ideas of the week. Topics include hashing, dimension reduction and LSH, boosting, linear programming, gradient descent, sampling and estimation, and an introduction to spectral techniques.

CS 170: Stanford Laptop Orchestra: Composition, Coding, and Performance (MUSIC 128) (15)

Classroom instantiation of the Stanford Laptop Orchestra (SLOrk) which includes public performances. An ensemble of more than 20 humans, laptops, controllers, and special speaker arrays designed to provide each computer-mediated instrument with its sonic identity and presence. Topics and activities include issues of composing for laptop orchestras, instrument design, sound synthesis, programming, and live performance. May be repeated four times for credit. Space is limited; see https://ccrma.stanford.edu/courses/128 for information about the application and enrollment process.

CS 173A: Foundations of Computational Human Genomics (34) science

(Only one of 173A or 273A counts toward any CS degree program.) A coder's primer to Computational Biology through the most amazing 'source code' known: your genome. Examine the major forces of genome 'code development' - positive, negative and neutral selection. Learn about genome sequencing (discovering your source code from fragments); genome content: variables (genes), control-flow (gene regulation), run-time stacks (epigenomics) and memory leaks (repeats); personalized genomics and genetic disease (code bugs); genome editing (code injection); ultra conservation (unsolved mysteries) and code modifications behind amazing animal adaptations. Course includes primers on molecular biology and text processing.

CS 177: Human Centered Product Management (34) humans

Ask any product person what the most important skills are for PMs and they'll say interpersonal dynamics-- negotiation, communication, conflict resolution, interviewing and more. This class will look at the role of product management through a human-centered lens, including customers and coworkers. As well, students will experience the Agile-Lean-UX development process. Course enrollment will be capped, an application will be sent out first day of class.

CS 181: Computers, Ethics, and Public Policy (4) impact

Ethical and social issues related to the development and use of computer technology. Ethical theory, and social, political, and legal considerations. Scenarios in problem areas: privacy, reliability and risks of complex systems, and responsibility of professionals for applications and consequences of their work.

CS 181W: Computers, Ethics, and Public Policy (WIM) (4) impact

Writing-intensive version of CS 181. Satisfies the WIM requirement for Computer Science, Engineering Physics, STS, and Math/Comp Sci undergraduates. To take this course, students need permission of instructor and may need to complete an assignment due at the first day of class. Please see https://CS 181.stanford.edu for more information.

CS 182: Ethics, Public Policy, and Technological Change (5) impact

Examination of recent developments in computing technology and platforms through the lenses of philosophy, public policy, social science, and engineering. Course is organized around five main units: algorithmic decision-making and bias; data privacy and civil liberties; artificial intelligence and autonomous systems; the power of private computing platforms; and issues of diversity, equity, and inclusion in the technology sector. Each unit considers the promise, perils, rights, and responsibilities at play in technological developments.

CS 182W: Ethics, Public Policy, and Technological Change (WIM) (5) impact

Writing-intensive version of CS 182. Satisfies the WIM requirement for Computer Science, Engineering Physics, STS, Math/Comp Sci, and Data Science undergraduates (and is only open to those majors). Prerequisite: CS 106A. See CS 182 for lecture day/time information. Enroll in either CS 182 or CS 182W, not both. Enrollment in WIM version of the course is limited to 125 students. Enrollment is restricted to seniors and coterminal students until January 9, 2023. Starting January 9, 2023, enrollment will open to all students if additional spaces remain available in the class.

CS 183E: Effective Leadership in High-Tech (1)

You will undoubtedly leave Stanford with the technical skills to excel in your first few jobs. But non-technical skills are just as critical to making a difference. This seminar is taught by two industry veterans in engineering leadership and product management. In a small group setting, we will explore how you can be a great individual contributor (communicating with clarity, getting traction for your ideas, resolving conflict, and delivering your best work) and how you can transition into leadership roles (finding leadership opportunities, creating a great team culture, hiring and onboarding new team members). We will end by turning back to your career (picking your first job and negotiating your offer, managing your career changes, building a great network, and succeeding with mentors).

CS 184: Bridging Policy and Tech Through Design (34) impact

This project-based course aims to bring together students from computer science and the social sciences to work with external partner organizations at the nexus of digital technology and public policy. Students will collaborate in interdisciplinary teams on a problem with a partner organization. Along with the guidance of faculty mentors and the teaching staff, students will engage in a project with outcomes ranging from policy memos and white papers to data visualizations and software. Possible projects suggested by partner organizations will be presented at an information session in early March. Following the infosession, a course application will open for teams to be selected before the start of Spring Quarter. Students may apply to a project with a partner organization or with a preformed team and their own idea to be reviewed for approval by the course staff. There will be one meeting per week for the full class and at least one weekly meeting with the project-based team mentors.

CS 185: Coding with LLM Assistants (2) ai

In under a year, LLM assistants have become a tool that many professional software engineers can't imagine living without. In this course, we will explore that phenomenon and design curriculum and pedagogical adaptations to it. In this class, we will: Conduct a survey-based ethnography of how professional software engineers are using LLMs (e.g., do they find it more useful for architectural planning vs code creation vs code explanation vs identifying bugs; what percentage of the day are they using it; how comfortable do they feel using it to work in frameworks or languages they are themselves unfamiliar with, etc); Engage in structured exploration using different LLM coding assistant tools for actual Stanford assignments (in classes they've already completed) and to perform new tasks in unfamiliar languages, and reflect on those experiences; Read what others are saying about the process of coding with LLMs through review of popular sources (e.g., podcasts, blog posts); Learn an overview of the science of teaching and learning, and what is needed for an effective education in software engineering; Design new curricular materials that address the new needs and practices of professional software engineers, using principles of good pedagogical design.

CS 187: Design for Advocacy (34) humans

The COVID pandemic has both revealed many of our underlying civilization problems and unleashed a desire for radical change. Effective responses will require people who know how to collaborate creatively and confidently, and act in systems with self-awareness. In this project based course, we will embrace complexity without being paralyzed by it. Working on a real-world challenge related to social health and civic fabric (e.g. political polarization, loneliness and social isolation) you will practice identifying high-leverage entry points for change, rigorously framing problems, and making process and product development decisions by evaluating impact. The course draws from HCD, systems thinking, strategic foresight, emotional intelligence, and agile team operations to prepare you to be even more successful as a designer, researcher, product manager, entrepreneur, or activist. If you tend to be more theory oriented, this course will get you into action. If you're quick to action, this course will give you a wider foundation for making a positive impact.

CS 190: Software Design Studio (34) softeng

This course teaches the art of software design: how to decompose large complex systems into classes that can be implemented and maintained easily. Topics include the causes of complexity, modular design, techniques for creating deep classes, minimizing the complexity associated with exceptions, in-code documentation, and name selection. The class involves significant system software implementation and uses an iterative approach consisting of implementation, review, and revision. The course is taught in a studio format with in-class discussions and code reviews in addition to lectures.

CS 191: Senior Project (16) capstone

Restricted to Computer Science students. Group or individual research projects under faculty direction. Register using instructor's section number. A project can be either a significant software application or publishable research. Software application projects include a research component, substantial programming, and are comparable in scale to shareware programs or commercial applications. Research projects may result in a paper publishable in an academic journal or presentable at a conference. Public presentation of final application or research results is required.

CS 191W: Writing Intensive Senior Research Project (36) capstone

Restricted to Computer Science students. Writing-intensive version of CS 191. Register using instructor's section number.

CS 192: Programming Service Project (14)

Restricted to Computer Science students. Appropriate academic credit (without financial support) is given for volunteer computer programming work of public benefit and educational value. Register using the section number associated with the instructor.

CS 193C: Client-Side Internet Technologies (3) sys

Client-side technologies used to create web sites such as Google maps or Gmail. Includes HTML5, CSS, JavaScript, the Document Object Model (DOM), and Ajax.

CS 193P: iOS Application Development (3)

Build mobile applications using tools and APIs in iOS. Developing applications for the iPhone and iPad requires integration of numerous concepts including functional programming, object-oriented programming, computer-human interfaces, graphics, animation, reactive interfaces, Model-View-Intent (MVI) and Model-View-View-Model (MVVM) design paradigms, object-oriented databases, networking, and interactive performance considerations including multi-threading. This course will require you to learn a new programming language (Swift) as well as the iOS development environment, SwiftUI.

CS 193Q: Introduction to Python Programming (1) intro

CS 193Q teaches basic Python programming with a similar end-condition to CS 106AP: strings, lists, numbers, dicts, loops, logic, functions, testings, decomposition and style, and modules. CS 193Q assumes knowledge of some programming language, and proceeds by showing how each common programming idea is expressed in Python. CS 193Q moves very quickly, meeting 3 times for 4 hours for a total of 12 hours which is a mixture of lecture and lab time.

CS 193U: Video Game Development in C++ and Unreal Engine (3)

Hands-on game development in C++ using Unreal Engine 4, the game engine that triple-A games like Fortnite, PUBG, and Gears of War are all built on. Students will be introduced to the Unreal editor, game frameworks, physics, AI, multiplayer and networking, UI, and profiling and optimization. Project-based course where you build your own games and gain a solid foundation in Unreal's architecture that will apply to any future game projects.

CS 193X: Web Programming Fundamentals (3) sys

Introduction to full-stack web development with an emphasis on fundamentals. Client-side topics include layout and rendering through HTML and CSS, event-driven programming through JavaScript, and single-threaded asynchronous programming techniques including Promises. Focus on modern standardized APIs and best practices. Server-side topics include the development of RESTful APIs, JSON services, and basic server-side storage techniques. Covers desktop and mobile web development.

CS 194: Software Project (3) capstone

Design, specification, coding, and testing of a significant team programming project under faculty supervision. Documentation includes capture of project rationale, design and discussion of key performance indicators, a weekly progress log and a software architecture diagram. Public demonstration of the project at the end of the quarter. Preference given to seniors. May be repeated for credit.

CS 194A: Android Programming Workshop (1) intro

Learn basic, foundational techniques for developing Android mobile applications and apply those toward building a single or multi page, networked Android application.

CS 194H: User Interface Design Project (34) capstone

Advanced methods for designing, prototyping, and evaluating user interfaces to computing applications. Novel interface technology, advanced interface design methods, and prototyping tools. Substantial, quarter-long course project that will be presented in a public presentation.

CS 194W: Software Project (WIM) (3) capstone

Restricted to Computer Science and Electrical Engineering undergraduates. Writing-intensive version of CS 194. Preference given to seniors.

CS 195: Supervised Undergraduate Research (34) special

Directed research under faculty supervision. Register using instructor's section number. Students are required to submit a written report and give a public presentation on their work.

CS 197: Computer Science Research (34) special

An onramp for students interested in breaking new ground in the frontiers of computer science. Course format features faculty lectures introducing the fundamentals of computer science research, alongside special interest group meetings that provide mentorship and feedback on a real research project.

CS 197C: Computer Science Research: CURIS Internship Onramp (3) special

A version of CS 197 designed specifically for students who will be participating in spring/summer CURIS internships OR have an ongoing research project with a (Ph.D. student or professor) mentor in the Stanford Computer Science department.

CS 198: Teaching Computer Science (34) special

Students lead a discussion section of 106A while learning how to teach a programming language at the introductory level. Focus is on teaching skills, techniques, and course specifics. Application and interview required; see http://CS 198.stanford.edu.

CS 198B: Additional Topics in Teaching Computer Science (1) special

Students build on the teaching skills developed in CS 198. Focus is on techniques used to teach topics covered in CS 106B.

CS 199: Independent Work (16) special

Special study under faculty direction, usually leading to a written report. Register using instructor's section number. Letter grade; if not appropriate, enroll in CS 199P.

CS 199P: Independent Work (16) special

Special study under faculty direction, usually leading to a written report. Register using instructor's section number. CR/NC only, if not appropriate, enroll in CS 199.

CS 202: Law for Computer Science Professionals (1) impact

Businesses are built on ideas. Today's successful companies are those that most effectively generate, protect, and exploit new and valuable business ideas...

CS 204: Computational Law (23) impact

Computational Law is an innovative approach to legal informatics concerned with the representation of regulations in computable form...

CS 205L: Continuous Mathematical Methods with an Emphasis on Machine Learning (3) math

A survey of numerical approaches to the continuous mathematics used throughout computer science with an emphasis on machine and deep learning...

CS 206: Exploring Computational Journalism (COMM 281) (3) special

This project-based course will explore the field of computational journalism, including the use of Data Science, Info Visualization, AI, and emerging technologies to help journalists discover and tell stories, understand their audience, advance free speech, and build trust. This course is repeatable for credit; enrollment priority given to students taking it for the first time.

CS 207: Antidiscrimination Law and Algorithmic Bias (3) impact

Human decision making is increasingly being displaced by algorithms. Judges sentence defendants based on 'risk scores;' regulators take enforcement actions based on predicted violations; advertisers target materials based on demographic attributes; and employers evaluate applicants and employees based on machine-learned models. A predominant concern with the rise of such algorithmic decision making (machine learning or artificial intelligence) is that it may replicate or exacerbate human bias. Algorithms might discriminate, for instance, based on race or gender. This course surveys the legal principles for assessing bias of algorithms, examines emerging techniques for how to design and assess bias of algorithms, and assesses how antidiscrimination law and the design of algorithms may need to evolve to account for the potential emergence of machine bias. Admission is by consent of instructor and is limited to 20 students. Student assessment is based on class participation, response papers, and a final project.

CS 208E: Great Ideas in Computer Science (3)

Great Ideas in Computer Science Covers the intellectual tradition of computer science emphasizing ideas that reflect the most important milestones in the history of the discipline. Topics include programming and problem solving; implementing computation in hardware; algorithmic efficiency; the theoretical limits of computation; cryptography and security; computer networks; machine learning; and the philosophy behind artificial intelligence. Readings will include classic papers along with additional explanatory material.

CS 210A: Software Project Experience with Corporate Partners (34) softeng

Two-quarter project course. Focus is on real-world software development. Corporate partners seed projects with loosely defined challenges from their R&D labs; students innovate to build their own compelling software solutions. Student teams are treated as start-up companies with a budget and a technical advisory board comprised of instructional staff and corporate liaisons. Teams will typically travel to the corporate headquarters of their collaborating partner, meaning some teams will travel internationally. Open loft classroom format such as found in Silicon Valley software companies. Exposure to: current practices in software engineering; techniques for stimulating innovation; significant development experience with creative freedoms; working in groups; real-world software engineering challenges; public presentation of technical work; creating written descriptions of technical work.

CS 210B: Software Project Experience with Corporate Partners (34) capstone

Continuation of CS 210A. Focus is on real-world software development. Corporate partners seed projects with loosely defined challenges from their R&D labs; students innovate to build their own compelling software solutions. Student teams are treated as start-up companies with a budget and a technical advisory board comprised of the instructional staff and corporate liaisons. Teams will typically travel to the corporate headquarters of their collaborating partner, meaning some teams will travel internationally. Open loft classroom format such as found in Silicon Valley software companies. Exposure to: current practices in software engineering; techniques for stimulating innovation; significant development experience with creative freedoms; working in groups; real world software engineering challenges; public presentation of technical work; creating written descriptions of technical work.

CS 212: Operating Systems and Systems Programming (35) sys

CS 214: Selected Reading of Computer Science Research (3) special

CS 217: Hardware Accelerators for Machine Learning (34) sys

This course provides in-depth coverage of the architectural techniques used to design accelerators for training and inference in machine learning systems. This course will cover classical ML algorithms such as linear regression and support vector machines as well as DNN models such as convolutional neural nets, and recurrent neural nets. We will consider both training and inference for these models and discuss the impact of parameters such as batch size, precision, sparsity and compression on the accuracy of these models. We will cover the design of accelerators for ML model inference and training. Students will become familiar with hardware implementation techniques for using parallelism, locality, and low precision to implement the core computational kernels used in ML. To design energy-efficient accelerators, students will develop the intuition to make trade-offs between ML model parameters and hardware implementation techniques. Students will read recent research papers and complete a design project.

CS 221: Artificial Intelligence: Principles and Techniques (34) ai

Artificial intelligence (AI) has had a huge impact in many areas, including medical diagnosis, speech recognition, robotics, web search, advertising, and scheduling. This course focuses on the foundational concepts that drive these applications. In short, AI is the mathematics of making good decisions given incomplete information (hence the need for probability) and limited computation (hence the need for algorithms). Specific topics include search, constraint satisfaction, game playing,n Markov decision processes, graphical models, machine learning, and logic.

CS 223A: Introduction to Robotics (ME 320) (3) ai

Robotics foundations in modeling, design, planning, and control. Class covers relevant results from geometry, kinematics, statics, dynamics, motion planning, and control, providing the basic methodologies and tools in robotics research and applications. Concepts and models are illustrated through physical robot platforms, interactive robot simulations, and video segments relevant to historical research developments or to emerging application areas in the field.

CS 224C: NLP for Computational Social Science (3) ai

We live in an era where many aspects of our social interactions are recorded as textual data, from social media posts to medical and financial records. This course is about using a variety of techniques from machine learning and theories from social science to study human behaviors and important societal questions at scale. Topics will include methods for natural language processing and causal inference, and their applications to important societal questions around hate speech, misinformation, and social movements.

CS 224G: Apps With LLMs Inside (3) ai

With ChatGPT, neural networks have had their Lisp moment. Conversation has become code and the model is the CPU for this ultimate programming language. A new universe of App development has opened up, and there are no guides for it, yet. This is a project course designed to explore the space of Apps built around LLMs, starting by playing with them, learning their limitations, and then applying a set of techniques to program them efficiently and effectively. Assignments are due on a two week 'sprint' cadence to mimic a startup style environment. Guest lectures by area experts provide industry perspective.

CS 224N: Natural Language Processing with Deep Learning (34) ai

Methods for processing human language information and the underlying computational properties of natural languages. Focus on deep learning approaches: understanding, implementing, training, debugging, visualizing, and extending neural network models for a variety of language understanding tasks. Exploration of natural language tasks ranging from simple word level and syntactic processing to coreference, question answering, and machine translation. Examination of representative papers and systems and completion of a final project applying a complex neural network model to a large-scale NLP problem.

CS 224R: Deep Reinforcement Learning (3) ai

Humans, animals, and robots faced with the world must make decisions and take actions in the world. Moreover, the decisions they choose affect the world they exist in - and those outcomes must be taken into account. This course is about algorithms for deep reinforcement learning - methods for learning behavior from experience, with a focus on practical algorithms that use deep neural networks to learn behavior from high-dimensional observations. Topics will include methods for learning from demonstrations, both model-based and model-free deep RL methods, methods for learning from offline datasets, and more advanced techniques for learning multiple tasks such as goal-conditioned RL, meta-RL, and unsupervised skill discovery. These methods will be instantiated with examples from domains with high-dimensional state and action spaces, such as robotics, visual navigation, and control.

CS 224S: Spoken Language Processing (24) ai

Introduction to spoken language technology with an emphasis on dialogue and conversational systems. Deep learning and other methods for automatic speech recognition, speech synthesis, affect detection, dialogue management, and applications to digital assistants and spoken language understanding systems.

CS 224U: Natural Language Understanding (34) ai

Project-oriented class focused on developing systems and algorithms for robust machine understanding of human language. Draws on theoretical concepts from linguistics, natural language processing, and machine learning. Topics include lexical semantics, distributed representations of meaning, relation extraction, semantic parsing, sentiment analysis, and dialogue agents, with special lectures on developing projects, presenting research results, and making connections with industry.

CS 224V: Conversational Virtual Assistants with Deep Learning (34) ai

Generative AI, and in particular Large Language Models (LLMs), has already changed how we work and study. But this is just the beginning, as it has the potential of assisting and perhaps eventually automating knowledge workers in all areas, from law, medicine, to teaching and mental health therapists. This course will focus on the general principles and the latest research on methodologies and tools that can be applied to all domains. This is a project-oriented course, where students will gain hands-on experience in either methodology research or applying the concepts to create useful assistants for a domain of their choice.

CS 224W: Machine Learning with Graphs (34) ai

Many complex data can be represented as a graph of relationships between objects. Such networks are a fundamental tool for modeling complex social, technological, and biological systems. This course focuses on the computational, algorithmic, and modeling challenges specific to the analysis of massive graphs. By means of studying the underlying graph structure and its features, students are introduced to machine learning techniques and data mining tools apt to reveal insights on a variety of networks. Topics include: representation learning and Graph Neural Networks; algorithms for the World Wide Web; reasoning over Knowledge Graphs; influence maximization; disease outbreak detection, social network analysis.

CS 225: Machine Learning for Discrete Optimization (MS&E 236) (3) ai

Machine learning has become a powerful tool for discrete optimization. This is because, in practice, we often have ample data about the application domain?data that can be used to optimize algorithmic performance, ranging from runtime to solution quality. This course covers how machine learning can be used within the discrete optimization pipeline from many perspectives, including how to design novel combinatorial algorithms with machine-learned modules and configure existing algorithms? parameters to optimize performance. Topics will include both applied machinery (such as graph neural networks, reinforcement learning, transformers, and LLMs) as well as theoretical tools for providing provable guarantees.

CS 225A: Experimental Robotics (3) ai

Hands-on laboratory course experience in robotic manipulation. Topics include robot kinematics, dynamics, control, compliance, sensor-based collision avoidance, and human-robot interfaces. Second half of class is devoted to final projects using various robotic platforms to build and demonstrate new robot task capabilities. Previous projects include the development of autonomous robot behaviors of drawing, painting, playing air hocket, yoyo, basketball, ping-pong or xylophone.

CS 226: The Future of Mechanical Engineering (ME 228) (1) science

This seminar series provides an overview of current and emerging research topics in mechanical engineering and its application to engineering and scientific problems. The seminar is targeted at senior mechanical engineering undergraduates and mechanical engineering graduate students. Presenters will be selected external speakers who feature exciting and cutting-edge research of mechanical engineering.

CS 227A: Robot Perception: Hardware, Algorithm, and Application (EE 227) (34) ai

Robot Perception is the cornerstone of modern robotics, enabling machines to interpret, understand, and respond to an array of sensory information they encounter. In the course, students will study the basic principles of typical sensor hardware on a robotics system (e.g., vision, tactile, and acoustic sensors), the algorithms that process the raw sensory data, and make actionable decisions from that information. Over the course of the semester, students will incrementally build their own vision-based robotics system in simulation via a series of homework coding assignments. Students enrolling 4 units will be required to submit an additional final written report.

CS 227B: General Game Playing (3) ai

A general game playing system accepts a formal description of a game to play it without human intervention or algorithms designed for specific games. Hands-on introduction to these systems and artificial intelligence techniques such as knowledge representation, reasoning, learning, and rational behavior. Students create GGP systems to compete with each other and in external competitions.

CS 228: Probabilistic Graphical Models: Principles and Techniques (34) ai

Probabilistic graphical modeling languages for representing complex domains, algorithms for reasoning using these representations, and learning these representations from data. Topics include: Bayesian and Markov networks, extensions to temporal modeling such as hidden Markov models and dynamic Bayesian networks, exact and approximate probabilistic inference algorithms, and methods for learning models from data. Also included are sample applications to various domains including speech recognition, biological modeling and discovery, medical diagnosis, message encoding, vision, and robot motion planning.

CS 229: Machine Learning (34) ai

Topics: statistical pattern recognition, linear and non-linear regression, non-parametric methods, exponential family, GLMs, support vector machines, kernel methods, deep learning, model/feature selection, learning theory, ML advice, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search.

CS 229B: Machine Learning for Sequence Modeling (34) ai

Sequence data and time series are becoming increasingly ubiquitous in fields as diverse as bioinformatics, neuroscience, health, environmental monitoring, finance, speech recognition/generation, video processing, and natural language processing. Machine learning has become an indispensable tool for analyzing such data; in fact, sequence models lie at the heart of recent progress in AI like GPT3. This class integrates foundational concepts in time series analysis with modern machine learning methods for sequence modeling. Connections and key differences will be highlighted, as well as how grounding modern neural network approaches with traditional interpretations can enable powerful leaps forward. You will learn theoretical fundamentals, but the focus will be on gaining practical, hands-on experience with modern methods through real-world case studies. You will walk away with a broad and deep perspective of sequence modeling and key ways in which such data are not just 1D images.

CS 229M: Machine Learning Theory (3) ai

How do we use mathematical thinking to design better machine learning methods? This course focuses on developing mathematical tools for answering this question. This course will cover fundamental concepts and principled algorithms in machine learning, particularly those that are related to modern large-scale non-linear models. The topics include concentration inequalities, generalization bounds via uniform convergence, non-convex optimization, implicit regularization effect in deep learning, and unsupervised learning and domain adaptations.

CS 229S: Systems for Machine Learning (3) sys

Deep learning and neural networks are being increasingly adopted across industries. They are now used to serve billions of users across applications such as search, knowledge discovery, and productivity assistants. As models become more capable and intelligent, this trend of large-scale adoption will continue to grow rapidly. Due to the widespread application, there is an increasing need to achieve high performance for both training and serving deep-learning models. However, performance is hindered by a multitude of infrastructure and lifecycle hurdles - the increasing complexity of the models, massive sizes of training and inference data, heterogeneity of the available accelerators and multi-node platforms, and diverse network properties. The slow adaptation of systems to new algorithms creates a bottleneck for the rapid evolution of deep-learning models and their applications. This course will cover systems approaches for improving the efficiency of machine learning pipelines - comprising data preparation, model training, and model deployment & inference -at each level of the systems stack spanning software and hardware.

CS 230: Deep Learning (34) ai

Deep Learning is one of the most highly sought after skills in AI. We will help you become good at Deep Learning. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach. AI is transforming multiple industries. After this course, you will likely find creative ways to apply it to your work. This class is taught in the flipped-classroom format. You will watch videos and complete in-depth programming assignments and online quizzes at home, then come in to class for advanced discussions and work on projects. This class will culminate in an open-ended final project, which the teaching team will help you on.

CS 231A: Computer Vision: From 3D Perception to 3D Reconstruction and Beyond (34) ai

An introduction to the concepts and applications in computer vision. Topics include: cameras and projection models, low-level image processing methods such as filtering and edge detection; mid-level vision topics such as segmentation and clustering; shape reconstruction from stereo, as well as high-level vision tasks such as object recognition, scene recognition, face detection and human motion categorization.

CS 231C: Computer Vision and Image Analysis of Art (3) ai

This course presents the application of rigorous image processing, computer vision, machine learning, computer graphics and artificial intelligence techniques to problems in the history and interpretation of fine art paintings, drawings, murals and other two-dimensional works, including abstract art.

CS 231N: Deep Learning for Computer Vision (34) ai

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars.

CS 233: Geometric and Topological Data Analysis (CME 251) (3) ai

Mathematical and computational tools for the analysis of data with geometric content, such images, videos, 3D scans, GPS traces -- as well as for other data embedded into geometric spaces.

CS 234: Reinforcement Learning (3) ai

To realize the dreams and impact of AI requires autonomous systems that learn to make good decisions. Reinforcement learning is one powerful paradigm for doing so, and it is relevant to an enormous range of tasks, including robotics, game playing, consumer modeling and healthcare. This class will briefly cover background on Markov decision processes and reinforcement learning, before focusing on some of the central problems, including scaling up to large domains and the exploration challenge. One key tool for tackling complex RL domains is deep learning and this class will include at least one homework on deep reinforcement learning.

CS 235: Computational Methods for Biomedical Image Analysis and Interpretation (BIOMEDIN 260, BMP 260, RAD 260) (34) sci

The latest biological and medical imaging modalities and their applications in research and medicine. Focus is on computational analytic and interpretive approaches to optimize extraction and use of biological and clinical imaging data for diagnostic and therapeutic translational medical applications. Topics include major image databases, fundamental methods in image processing and quantitative extraction of image features, structured recording of image information including semantic features and ontologies, indexing, search and content-based image retrieval. Case studies include linking image data to genomic, phenotypic and clinical data, developing representations of image phenotypes for use in medical decision support and research applications and the role that biomedical imaging informatics plays in new questions in biomedical science. Includes a project. Enrollment for 3 units requires instructor consent.

CS 236: Deep Generative Models (3) ai

Generative models are widely used in many subfields of AI and Machine Learning. Recent advances in parameterizing these models using neural networks, combined with progress in stochastic optimization methods, have enabled scalable modeling of complex, high-dimensional data including images, text, and speech. In this course, we will study the probabilistic foundations and learning algorithms for deep generative models, including Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), and flow models. The course will also discuss application areas that have benefitted from deep generative models, including computer vision, speech and natural language processing, and reinforcement learning.

CS 236G: Generative Adversarial Networks (3) ai

Generative Adversarial Networks (GANs) have rapidly emerged as the state-of-the-art technique in realistic image generation. This course presents theoretical intuition and practical knowledge on GANs, from their simplest to their state-of-the-art forms. Their benefits and applications span realistic image editing that is omnipresent in popular app filters, enabling tumor classification under low data schemes in medicine, and visualizing realistic scenarios of climate change destruction. This course also examines key challenges of GANs today, including reliable evaluation, inherent biases, and training stability. After this course, students should be familiar with GANs and the broader generative models and machine learning contexts in which these models are situated.

CS 237A: Principles of Robot Autonomy I (AA 274A, EE 260A) (3) ai

CS 237B: Principles of Robot Autonomy II (34) ai

This course teaches advanced principles for endowing mobile autonomous robots with capabilities to autonomously learn new skills and to physically interact with the environment and with humans. It also provides an overview of different robot system architectures. Concepts that will be covered in the course are: Reinforcement Learning and its relationship to optimal control, contact and dynamics models for prehensile and non-prehensile robot manipulation, imitation learning and human intent inference, as well as different system architectures and their verification. Students will earn the theoretical foundations for these concepts and implement them on mobile manipulation platforms. In homeworks, the Robot Operating System (ROS) will be used extensively for demonstrations and hands-on activities.

CS 238: Decision Making under Uncertainty (34) ai

This course is designed to increase awareness and appreciation for why uncertainty matters, particularly for aerospace applications. Introduces decision making under uncertainty from a computational perspective and provides an overview of the necessary tools for building autonomous and decision-support systems. Following an introduction to probabilistic models and decision theory, the course will cover computational methods for solving decision problems with stochastic dynamics, model uncertainty, and imperfect state information. Topics include: Bayesian networks, influence diagrams, dynamic programming, reinforcement learning, and partially observable Markov decision processes. Applications cover: air traffic control, aviation surveillance systems, autonomous vehicles, and robotic planetary exploration.

CS 239: Advanced Topics in Sequential Decision Making (34) ai

Survey of recent research advances in intelligent decision making for dynamic environments from a computational perspective. Efficient algorithms for single and multiagent planning in situations where a model of the environment may or may not be known. Partially observable Markov decision processes, approximate dynamic programming, and reinforcement learning. New approaches for overcoming challenges in generalization from experience, exploration of the environment, and model representation so that these methods can scale to real problems in a variety of domains including aerospace, air traffic control, and robotics. Students are expected to produce an original research paper on a relevant topic.

CS 240: Advanced Topics in Operating Systems (3) sys

Recent research. Classic and new papers. Topics: virtual memory management, synchronization and communication, file systems, protection and security, operating system extension techniques, fault tolerance, and the history and experience of systems programming.

CS 240LX: Advanced Systems Laboratory, Accelerated (3) sys

This is an implementation-heavy, lab-based class that covers similar topics as CS 240, but by writing code versus discussing papers. Our code will run 'bare-metal' (without an operating system) on the widely-used ARM-based raspberry pi. Bare-metal lets us do interesting tricks without constantly fighting a lumbering, general-purpose OS that cannot get out of its own way. We will do ten projects, one per week, where each project covers two labs of (at a minimum) several hours each and a non-trivial amount of outside work. The workload is significant, but I will aim to not waste your time.

CS 241: Embedded Systems Workshop (3) sys

Project-centric building hardware and software for embedded computing systems. This year the course projects are on a large interactive light sculpture to be installed in Packard. Syllabus topics will be determined by the needs of the enrolled students and projects. Examples of topics include: interrupts and concurrent programming, mechanical control, state-based programming models, signaling and frequency response, mechanical design, power budgets, software, firmware, and PCB design. Interested students can help lead community workshops to begin building the installation.

CS 242: Programming Languages (34) pls

This course explores foundational models of computation, such as the lambda calculus and other small calculi, and the incorporation of basic advances in PL theory into modern programming languages such as Haskell and Rust. Topics include type systems (polymorphism, algebraic data types, static vs. dynamic), control flow (exceptions, continuations), concurrency/parallelism, metaprogramming, verification, and the semantic gap between computational models and modern hardware. The study of programming languages is equal parts systems and theory, looking at how a rigorous understanding of the semantics of computation enables formal reasoning about the behavior and properties of complex real-world systems.

CS 243: Program Analysis and Optimizations (34) pls

Program analysis techniques used in compilers and software development tools to improve productivity, reliability, and security. The methodology of applying mathematical abstractions such as graphs, fixpoint computations, binary decision diagrams in writing complex software, using compilers as an example. Topics include data flow analysis, instruction scheduling, register allocation, parallelism, data locality, interprocedural analysis, and garbage collection.

CS 244: Advanced Topics in Networking (34) sys

Classic papers, new ideas, and research papers in networking. Architectural principles: why the Internet was designed this way? Congestion control. Wireless and mobility; software-defined networks (SDN) and network virtualization; content distribution networks; packet switching; data-center networks.

CS 244B: Distributed Systems (3) sys

Distributed operating systems and applications issues, emphasizing high-level protocols and distributed state sharing as the key technologies. Topics: distributed shared memory, object-oriented distributed system design, distributed directory services, atomic transactions and time synchronization, application-sufficient consistency, file access, process scheduling, process migration, and storage/communication abstractions on distribution, scale, robustness in the face of failure, and security.

CS 245: Principles of Data-Intensive Systems (34) sys

Most important computer applications have to reliably manage and manipulate datasets. This course covers the architecture of modern data storage and processing systems, including relational databases, cluster computing frameworks, streaming systems and machine learning systems. Topics include storage management, query optimization, transactions, concurrency, fault recovery, and parallel processing, with a focus on the key design ideas shared across many types of data-intensive systems.

CS 246: Mining Massive Data Sets (34) ai

The availability of massive datasets is revolutionizing science and industry. This course discusses data mining and machine learning algorithms for analyzing very large amounts of data. Topics include: Big data systems (Hadoop, Spark); Link Analysis (PageRank, spam detection); Similarity search (locality-sensitive hashing, shingling, min-hashing); Stream data processing; Recommender Systems; Analysis of social-network graphs; Association rules; Dimensionality reduction (UV, SVD, and CUR decompositions); Algorithms for large-scale mining (clustering, nearest-neighbor search); Large-scale machine learning (decision tree ensembles); Multi-armed bandit; Computational advertising.

CS 247A: Design for Artificial Intelligence (SYMSYS 195A) (34) humans

A project-based course that builds on the introduction to design in CS 147 by focusing on advanced methods and tools for research, prototyping, and user interface design. Studio based format with intensive coaching and iteration to prepare students for tackling real world design problems. This course takes place entirely in studios; you must plan on attending every studio to take this class. The focus of CS 247A is design for human-centered artificial intelligence experiences. What does it mean to design for AI? What is HAI? How do you create responsible, ethical, human centered experiences? Let us explore what AI actually is and the constraints, opportunities and specialized processes necessary to create AI systems that work effectively for the humans involved.

CS 247B: Design for Behavior Change (SYMSYS 195B) (34) humans

Over the last decade, tech companies have invested in shaping user behavior, sometimes for altruistic reasons like helping people change bad habits into good ones, and sometimes for financial reasons such as increasing engagement. In this project-based hands-on course, students explore the design of systems, information and interface for human use. We will model the flow of interactions, data and context, and crafting a design that is useful, appropriate and robust. Students will design and prototype utility apps or games as a response to the challenges presented. We will also examine the ethical consequences of design decisions and explore current issues arising from unintended consequences.

CS 247G: Design for Play (SYMSYS 195G) (34) humans

A project-based course that builds on the introduction to design in CS 147 by focusing on advanced methods and tools for research, prototyping, and user interface design. Studio based format with intensive coaching and iteration to prepare students for tackling real world design problems. This course takes place entirely in studios; please plan on attending every studio to take this class. The focus of CS 247g is an introduction to theory and practice of game design. We will make digital and paper games, do rapid iteration and run user research studies appropriate to game design. This class has multiple short projects, allowing us to cover a variety of genres, from narrative to pure strategy.

CS 247I: Design for Understanding (34) humans

Complex problems require nuanced design approaches. In this project-based hands-on course, students explore the design of systems, information and interface for human use. Each quarter we pick a different challenging topic to explore and explain; past classes have included fake news, electoral politics and gender. Students will create an explainer, an information site and a game as a response to the challenges presented. We will model the flow of interactions, data and context, and craft a design that is useful, appropriate and robust. We will also examine the ethical consequences of design decisions and explore current issues arising from unintended consequences.

CS 247S: Service Design (SYMSYS 195S) (34) humans

A project-based course that builds on the introduction to design in CS 147 by focusing on advanced methods and tools for research, prototyping, and user interface design. Studio based format with intensive coaching and iteration to prepare students for tackling real world design problems. This course takes place entirely in studios; you must plan on attending every studio to take this class. The focus of CS 247S is Service Design. In this course we will be looking at experiences that address the needs of multiple types of stakeholders at different touchpoints - digital, physical, and everything in between. If you have ever taken an Uber, participated in the Draw, engaged with your bank, or ordered a coffee through the Starbucks app, you have experienced a service that must have a coordinated experience for the customer, the service provider, and any other stakeholders involved. Let us explore what specialized tools and processes are required to created these multi-faceted interactions.

CS 248A: Computer Graphics: Rendering, Geometry, and Image Manipulation (34) graphics

This course provides a comprehensive introduction to interactive computer graphics, focusing on fundamental concepts and techniques, as well as their cross-cutting relationship to multiple problem domains in interactive graphics (such as rendering, animation, geometry, image processing). Topics include: 2D and 3D drawing, sampling theory, interpolation, rasterization, image compositing, the real-time GPU graphics pipeline (and parallel rendering), VR rendering, geometric transformations, curves and surfaces, geometric data structures, subdivision, meshing, spatial hierarchies, image processing, time integration, physically-based animation, and inverse kinematics. The course will involve several in-depth programming assignments and a self-selected final project that explores concepts covered in the class.

CS 248B: Fundamentals of Computer Graphics: Animation and Simulation (3) graphics

This course provides a comprehensive introduction to computer graphics, focusing on fundamental concepts and techniques in Computer Animation and Physics Simulation. Topics include numerical integration, 3D character modeling, keyframe animation, skinning/rigging, inverse kinematics, rigid body dynamics, deformable body simulation, and fluid simulation.

CS 249I: The Modern Internet (3) sys

Advanced networking course that covers how the Internet has evolved and operates today. Topics include modern Internet topology and routing practices, recently introduced network protocols, popular content delivery strategies, and pressing privacy, security, and abuse challenges. The course consists of a mixture of lecture, guest talks, and investigative projects where students will analyze how Internet operates in practice.

CS 250: Algebraic Error Correcting Codes (EE 387) (3) math

Introduction to the theory of error correcting codes, emphasizing algebraic constructions, and diverse applications throughout computer science and engineering. Topics include basic bounds on error correcting codes; Reed-Solomon and Reed-Muller codes; list-decoding, list-recovery and locality. Applications may include communication, storage, complexity theory, pseudorandomness, cryptography, streaming algorithms, group testing, and compressed sensing.

CS 251: Cryptocurrencies and blockchain technologies (3) sys

For advanced undergraduates and for graduate students. The potential applications for Bitcoin-like technologies is enormous. The course will cover the technical aspects of cryptocurrencies, blockchain technologies, and distributed consensus. Students will learn how these systems work, and how to engineer secure software that interacts with Blockchains like Bitcoin, Ethereum, and others.

CS 253: Web Security (3) sys

Principles of web security. The fundamentals and state-of-the-art in web security. Attacks and countermeasures. Topics include: the browser security model, web app vulnerabilities, injection, denial-of-service, TLS attacks, privacy, fingerprinting, same-origin policy, cross site scripting, authentication, JavaScript security, emerging threats, defense-in-depth, and techniques for writing secure code. Course projects include writing security exploits, defending insecure web apps, and implementing emerging web standards.

CS 254: Computational Complexity (3) theory

An introduction to computational complexity theory. Topics include the P versus NP problem and other major challenges of complexity theory; Space complexity: Savitch's theorem and the Immerman-Szelepscényi theorem; P, NP, coNP, and the polynomial hierarchy; The power of randomness in computation; Non-uniform computation and circuit complexity; Interactive proofs.

CS 254B: Computational Complexity II (3) theory

A continuation of CS 254 (Computational Complexity). Topics include Barriers to P versus NP; The relationship between time and space, and time-space tradeoffs for SAT; The hardness versus randomness paradigm; Average-case complexity; Fine-grained complexity; Current and new areas of complexity theory research.

CS 255: Introduction to Cryptography (3) sys

For advanced undergraduates and graduate students. Theory and practice of cryptographic techniques used in computer security. Topics: encryption (symmetric and public key), digital signatures, data integrity, authentication, key management, PKI, zero-knowledge protocols, and real-world applications.

CS 256: Algorithmic Fairness (3) ai

Machine learning and data analysis have enjoyed tremendous success in a broad range of domains. These advances hold the promise of great benefits to individuals, organizations and society. Undeniably, algorithms are informing decisions that reach ever more deeply into our lives, from news article recommendations to criminal sentencing decisions to healthcare diagnostics.

CS 257: Introduction to Automated Reasoning (3) ai

Automated logical reasoning has enabled substantial progress in many fields, including hardware and software verification, theorem-proving, and artificial intelligence. Different application scenarios may require different automated reasoning techniques and sometimes their combination.

CS 259Q: Quantum Computing (3) theory

This course introduces the basics of quantum computing. Topics include: qubits, entanglement, and non-local correlations; quantum gates, circuits, and compilation algorithms; basic quantum algorithms such as Simon's algorithm and Grover's algorithm; Shor's factoring algorithm and the hidden subgroup problem; Hamiltonian simulation; stabilizer circuits, the Gottesman-Knill theorem, and the basics of quantum error correction.

CS 261: Optimization and Algorithmic Paradigms (3) algs

Algorithms for network optimization: max-flow, min-cost flow, matching, assignment, and min-cut problems. Introduction to linear programming. Use of LP duality for design and analysis of algorithms. Approximation algorithms for NP-complete problems such as Steiner Trees, Traveling Salesman, and scheduling problems. Randomized algorithms. Introduction to sub-linear algorithms and decision making under uncertainty.

CS 263: Counting and Sampling (3) algs

This course will cover various algorithm design techniques for two intimately connected class of problems: sampling from complex probability distributions and counting combinatorial structures. A large part of the course will cover Markov Chain Monte Carlo techniques: coupling, stationary times, canonical paths, Poincare and log-Sobolev inequalities. Other topics include correlation decay in spin systems, variational techniques, holographic algorithms, and polynomial interpolation-based counting.

CS 265: Randomized Algorithms and Probabilistic Analysis (CME 309) (3) algs

Randomness pervades the natural processes around us, from the formation of networks, to genetic recombination, to quantum physics. Randomness is also a powerful tool that can be leveraged to create algorithms and data structures which, in many cases, are more efficient and simpler than their deterministic counterparts. This course covers the key tools of probabilistic analysis, and application of these tools to understand the behaviors of random processes and algorithms.

CS 269I: Incentives in Computer Science (MS&E 206) (3) entrepreneur

Many 21st-century computer science applications require the design of software or systems that interact with multiple self-interested participants. This course will provide students with the vocabulary and modeling tools to reason about such design problems. Emphasis will be on understanding basic economic and game theoretic concepts that are relevant across many application domains, and on case studies that demonstrate how to apply these concepts to real-world design problems.

CS 269O: Introduction to Optimization Theory (MS&E 213) (3) math

Introduction of core algorithmic techniques and proof strategies that underlie the best known provable guarantees for minimizing high dimensional convex functions. Focus on broad canonical optimization problems and survey results for efficiently solving them, ultimately providing the theoretical foundation for further study in optimization.

CS 270: Modeling Biomedical Systems (BIOMEDIN 210) (3) science

At the core of informatics is the problem of creating computable models of biomedical phenomena. This course explores methods for modeling biomedical systems with an emphasis on contemporary semantic technology, including knowledge graphs. Topics: data modeling, knowledge representation, controlled terminologies, ontologies, reusable problem solvers, modeling problems in healthcare information technology and other aspects of informatics. Students acquire hands-on experience with several systems and tools.

CS 272: Introduction to Biomedical Data Science Research Methodology (BIOE 212, BIOMEDIN 212, GENE 212) (35) capstone

Capstone Biomedical Data Science experience. Hands-on software building. Student teams conceive, design, specify, implement, evaluate, and report on a software project in the domain of biomedicine. Creating written proposals, peer review, providing status reports, and preparing final reports. Issues related to research reproducibility. Guest lectures from professional biomedical informatics systems builders on issues related to the process of project management. Software engineering basics. Because the team projects start in the first week of class, attendance that week is strongly recommended.

CS 273B: Deep Learning in Genomics and Biomedicine (BIODS 237, BIOMEDIN 273B, GENE 236) (3) ai

Recent breakthroughs in high-throughput genomic and biomedical data are transforming biological sciences into 'big data' disciplines. In parallel, progress in deep neural networks are revolutionizing fields such as image recognition, natural language processing and, more broadly, AI. This course explores the exciting intersection between these two advances.

CS 273C: Cloud Computing for Biology and Healthcare (BIOMEDIN 222, GENE 222) (3) sys

Big Data is radically transforming healthcare. To provide real-time personalized healthcare, we need hardware and software solutions that can efficiently store and process large-scale biomedical datasets. In this class, students will learn the concepts of cloud computing and parallel systems' architecture.

CS 274: Representations and Algorithms for Computational Molecular Biology (BIOE 214, BIOMEDIN 214, GENE 214) (34) science

This is a graduate level introduction to bioinformatics and computational biology, algorithms for alignment of biological sequences and structures, BLAST, phylogenetic tree construction, hidden Markov models, basic structural computations on proteins, protein structure prediction, molecular dynamics and energy minimization, statistical analysis of 3D structure, knowledge controlled terminologies for molecular function, expression analysis, chemoinformatics, pharmacogenetics, network biology.

CS 275: Translational Bioinformatics (34) science

Analytic and interpretive methods to optimize the transformation of genetic, genomic, and biological data into diagnostics and therapeutics for medicine. Topics: access and utility of publicly available data sources; types of genome-scale measurements in molecular biology and genomic medicine; linking genome-scale data to clinical data and phenotypes; and new questions in biomedicine using bioinformatics. Case studies.

CS 275A: Symbolic Musical Information (24) special

Properties of symbolic data for music applications including advanced notation systems, data durability, mark-up languages, optical music recognition, and data-translation tasks. Hands-on work involves these digital score formats: Guido Music Notation, Humdrum, MuseData, MEI, MusicXML, SCORE, and MIDI internal code.

CS 275B: Computational Music Analysis (24) special

Leveraging off three synchronized sets of symbolic data resources for notation and analysis, the lab portion introduces students to the open-source Humdrum Toolkit for music representation and analysis. Issues of data content and quality as well as methods of information retrieval, visualization, and summarization are considered in class. Grading based primarily on student projects.

CS 277: Foundation Models for Healthcare (3) ai

Generative AI and large-scale self-supervised foundation models are poised to have a profound impact on human decision making across occupations. Healthcare is one such area where such models have the capacity to impact patients, clinicians, and other care providers. In this course, we will explore the training, evaluation, and deployment of generative AI and foundation models, with a focus on addressing current and future medical needs. The course will cover models used in natural language processing, computer vision, and multi-modal applications. We will explore the intersection of models trained on non-healthcare domains and their adaptation to domain-specific problems, as well as healthcare-specific foundation models.

CS 278: Social Computing (34) humans

Today we interact with our friends and enemies, our team partners and romantic partners, and our organizations and societies, all through computational systems. How do we design these social computing systems - platforms for social media, online communities, and collaboration - to be effective and responsible? This course covers design patterns for social computing systems and the foundational ideas that underpin them.

CS 279: Computational Biology: Structure and Organization of Biomolecules and Cells (3) science

Computational techniques for investigating and designing the three-dimensional structure and dynamics of biomolecules and cells. These computational methods play an increasingly important role in drug discovery, medicine, bioengineering, and molecular biology. Course topics include protein structure prediction, protein design, drug screening, molecular simulation, cellular-level simulation, image analysis for microscopy, and methods for solving structures from crystallography and electron microscopy data.

CS 281: Ethics of Artificial Intelligence (34) impact

Machine learning has become an indispensable tool for creating intelligent applications, accelerating scientific discoveries, and making better data-driven decisions. Yet, the automation and scaling of such tasks can have troubling negative societal impacts. Through practical case studies, you will identify issues of fairness, justice and truth in AI applications. You will then apply recent techniques to detect and mitigate such algorithmic biases, along with methods to provide more transparency and explainability to state-of-the-art ML models. Finally, you will derive fundamental formal results on the limits of such techniques, along with tradeoffs that must be made for their practical application.

CS 288: Applied Causal Inference with Machine Learning and AI (3) ai

Fundamentals of modern applied causal inference. Basic principles of causal inference and machine learning and how the two can be combined in practice to deliver causal insights and policy implications in real world datasets, allowing for high-dimensionality and flexible estimation. Lectures will provide foundations of these new methodologies and the course assignments will involve real world data (from social science, tech industry and healthcare applications) and synthetic data analysis based on these methodologies.

CS 293: Empowering Educators via Language Technology (EDUC 473) (24) ai

This course explores the use of natural language processing (NLP) to support educators, by discovering, measuring, and analyzing high-leverage teaching practices. Topics include computational social science methods, ethics, bias and fairness, automated scoring, causal analyses, large language models, among others. Engaging with relevant papers, students will work towards a final project using NLP methods and a critical social scientific lens. Projects are pitched to a jury of educators at the end of the course.

CS 294S: Research Project in Software Systems and Security (3) capstone

Topics vary. Focus is on emerging research themes such as programmable open mobile Internet that spans multiple system topics such as human-computer interaction, programming systems, operating systems, networking, and security. May be repeated for credit.

CS 294W: Writing Intensive Research Project in Computer Science (3) communication

Restricted to Computer Science and Computer Systems Engineering undergraduates. Students enroll in the CS 294W section attached to the CS 294 project they have chosen.

CS 295: Software Engineering (3) softeng

Software specification, testing and verification. The emphasis is on automated tools for developing reliable software. The course covers material---drawn primarily from recent research papers---on the technology underlying these tools. Assignments supplement the lectures with hands-on experience in using these tools and customizing them for solving new problems. The course is appropriate for students intending to pursue research in program analysis and verification, as well as for those who wish to add the use of advanced software tools to their skill set.

CS 298: Seminar on Teaching Introductory Computer Science (EDUC 298) (1) intro

Faculty, undergraduates, and graduate students interested in teaching discuss topics raised by teaching computer science at the introductory level.

CS 300: Departmental Lecture Series (1) talks

Priority given to first-year Computer Science Ph.D. students. CS Masters students admitted if space is available. Presentations by members of the department faculty, each describing informally his or her current research interests and views of computer science as a whole.

CS 309: Industrial Lectureships in Computer Science (1)

Guest computer scientist. By arrangement. May be repeated for credit.

CS 309A: Cloud Computing Seminar (1) sys

For science, engineering, computer science, business, education, medicine, and law students. Cloud computing is bringing information systems out of the back office and making it core to the entire economy. Furthermore with the advent of smarter machines cloud computing will be integral to building a more precision planet. This class is intended for all students who want to begin to understand the implications of this technology. Guest industry experts are public company CEOs who are either delivering cloud services or using cloud services to transform their businesses.

CS 315B: Parallel Computing Research Project (3) sys

Advanced topics and new paradigms in parallel computing including parallel algorithms, programming languages, runtime environments, library debugging/tuning tools, and scalable architectures. Research project.

CS 320: Value of Data and AI (3) ai

Many of the most valuable companies in the world and the most innovative startups have business models based on data and AI, but our understanding about the economic value of data, networks and algorithmic assets remains at an early stage. For example, what is the value of a new dataset or an improved algorithm? How should investors value a data-centric business such as Netflix, Uber, Google, or Facebook? And what business models can best leverage data and algorithmic assets in settings as diverse as e-commerce, manufacturing, biotech and humanitarian organizations? In this graduate seminar, we will investigate these questions by studying recent research on these topics and by hosting in-depth discussions with experts from industry and academia. Key topics will include value of data quantity and quality in statistics and AI, business models around data, networks, scaling effects, economic theory around data, and emerging data protection regulations. Students will also conduct a group research projects in this field.

CS 322: Triangulating Intelligence: Melding Neuroscience, Psychology, and AI (3) ai

This course will cover both classic findings and the latest research progress on the intersection of cognitive science, neuroscience, and artificial intelligence: How does the study of minds and machines inform and guide each other? What are the assumptions, representations, or learning mechanisms that are shared (across multiple disciplines, and what are different? How can we build a synergistic partnership between cognitive psychology, neuroscience, and artificial intelligence? We will focus on object perception and social cognition (human capacities, especially in infancy and early childhood) and the ways in which these capacities are formalized and reverse-engineered (computer vision, reinforcement learning). Through paper reading and review, discussion, and the final project, students will learn the common foundations shared behind neuroscience, cognitive science, and AI research and leverage them to develop their own research project in these areas.

CS 323: The AI Awakening: Implications for the Economy and Society (34) ai

Intelligent computer agents must reason about complex, uncertain, and dynamic environments. This course is a graduate level introduction to automated reasoning techniques and their applications, covering logical and probabilistic approaches. Topics include: logical and probabilistic foundations, backtracking strategies and algorithms behind modern SAT solvers, stochastic local search and Markov Chain Monte Carlo algorithms, variational techniques, classes of reasoning tasks and reductions, and applications. Enrollment by application: https://digitaleconomy.stanford.edu/about/the-ai-awakening-implications-for-the-economy-and-society/

CS 323A: The AI Awakening: Implications for the Economy and Society (3) ai

This course offers an overview of blockchain governance and DAOs, including the governance of layer-1 blockchains, DAO tooling, on-chain and off-chain voting, delegation and constitutional design, identity, and privacy. We will cover these topics both from a technical perspective and from a social scientific perspective, and will include a range of guests from the web3 space.

CS 324: Advances in Foundation Models (3) ai

Foundation models (FMs) are transforming the landscape of AI in research and industry. Such models (e.g., GPT-3, CLIP, Stable Diffusion) are trained on large amounts of broad data and are adaptable to a wide range of downstream tasks. In this course, students will learn fundamentals behind the models and algorithms, systems and infrastructure, and ethics and societal impacts of foundation models, with an emphasis on gaining hands-on experience and identifying real-world use-cases for FMs. Students will hear from speakers in industry working on foundation models in the wild. The main class assignment will be a quarter-long final project, involving either researching the capabilities of FMs or building an FM-powered application.

CS 324H: History of Natural Language Processing (34) ai

Intellectual history of computational linguistics, natural language processing, and speech recognition, using primary sources. Reading of seminal early papers, interviews with early pioneers, with the goal of understanding the origins and intellectual development of the field.

CS 325B: Data for Sustainable Development (35) ai

The sustainable development goals (SDGs) encompass many important aspects of human and ecosystem well-being that are traditionally difficult to measure. This project-based course will focus on ways to use inexpensive, unconventional data streams to measure outcomes relevant to SDGs, including poverty, hunger, health, governance, and economic activity. Students will apply machine learning techniques to various projects outlined at the beginning of the quarter. The main learning goals are to gain experience conducting and communicating original research.

CS 326: Topics in Advanced Robotic Manipulation (34) ai

This course provides a survey of the most important and influential concepts in autonomous robotic manipulation. It includes classical concepts that are still widely used and recent approaches that have changed the way we look autonomous manipulation. We cover approaches towards motion planning and control using visual and tactile perception as well as machine learning. This course is especially concerned with new approaches for overcoming challenges in generalization from experience, exploration of the environment, and learning representation so that these methods can scale to real problems. Students are expected to present one paper in a tutorial, debate a paper once from the Pro and once from the Con side. They are also expected to propose an original research project and work on it towards a research paper.

CS 327A: Advanced Robotic Manipulation (ME 323) (3) ai

Advanced control methodologies and novel design techniques for complex human-like robotic and bio mechanical systems. Class covers the fundamentals in operational space dynamics and control, elastic planning, human motion synthesis. Topics include redundancy, inertial properties, haptics, simulation, robot cooperation, mobile manipulation, human-friendly robot design, humanoids and whole-body control. Additional topcs in emerging areas are presented by groups of students at the end-of-quarter mini-symposium.

CS 328: Foundations of Causal Machine Learning (3) ai

Theoretical foundations of modern techniques at the intersection of causal inference and machine learning. Topics may include: semi-parametric inference and semi-parametric efficiency, modern statistical learning theory, Neyman orthogonality and double/debiased machine learning, theoretical foundations of high-dimensional linear regression, theoretical foundations of non-linear regression models, such as random forests and neural networks, adaptive non-parametric estimation of conditional moment models, estimation and inference on heterogeneous treatment effects, causal inference and reinforcement learning, off-policy evaluation, adaptive experimentation and inference.

CS 329D: Machine Learning Under Distributional Shifts (3) ai

The progress of machine learning systems has seemed remarkable and inexorable a wide array of benchmark tasks including image classification, speech recognition, and question answering have seen consistent and substantial accuracy gains year on year. However, these same models are known to fail consistently on atypical examples and domains not contained within the training data. The goal of the course is to introduce the variety of areas in which distributional shifts appear, as well as provide theoretical characterization and learning bounds for distribution shifts.

CS 329E: Machine Learning on Embedded Systems (EE 292D) (3) ai

This is a project-based class where students will learn how to develop machine learning models for execution in resource constrained environments such as embedded systems. In this class students will learn about techniques to optimize machine learning models and deploy them on a device such as a Arduino, Raspberry PI, Jetson, or Edge TPUs. The class has a significant project component.

CS 329H: Machine Learning from Human Preferences (3) ai

Machine learning (ML) from human preferences provides mechanisms for capturing human feedback, which is used to design loss functions or rewards that are otherwise difficult to specify quantitatively, e.g., for socio-technical applications such as algorithmic fairness and many language and robotic tasks. While learning from human preferences has emerged as an increasingly important component of modern machine learning, e.g., credited with advancing the state of the art in language modeling and reinforcement learning, existing approaches are largely reinvented independently in each subfield, with limited connections drawn among them. This course will cover the foundations of learning from human preferences from first principles and outline connections to the growing literature on the topic. This includes: Inverse reinforcement learning, which uses human preferences to specify the reinforcement learning reward function; Metric elicitation, which uses human preferences to specify tradeoffs for cost-sensitive classification; Reinforcement learning from human feedback, where human preferences are used to align a pre-trained language model. This is a graduate-level course. By the end of the course, students should be able to understand and implement state-of-the-art learning from human feedback and be ready to conduct research on these topics.

CS 329M: Machine Programming (34) ai

The field of machine programming (MP) is concerned with the automation of software development. Given the recent advances in software algorithms, hardware efficiency and capacity, and an ever increasing availability of code data, it is now possible to train machines to help develop software. In this course, we teach students how to build real-world MP systems. We begin with a high-level overview of the field, including an abbreviated analysis of state-of-the-art (e.g., Merly Mentor). Next, we discuss the foundations of MP and the key areas for innovation, some of which are unique to MP. We close with a discussion of current limitations and future directions of MP. This course includes a nine-week hands-on project, where students (as individuals or in a small group) will create their own MP system and demonstrate it to the class.

CS 329P: Practical Machine Learning (34) ai

Applying Machine Learning (ML) to solve real problems accurately and robustly requires more than just training the latest ML model. First, you will learn practical techniques to deal with data. This matters since real data is often not independently and identically distributed. It includes detecting covariate, concept, and label shifts, and modeling dependent random variables such as the ones in time series and graphs. Next, you will learn how to efficiently train ML models, such as tuning hyper-parameters, model combination, and transfer learning. Last, you will learn about fairness and model explainability, and how to efficiently deploy models. This class will teach both statistics, algorithms and code implementations. Homeworks and the final project emphasize solving real problems.

CS 329R: Race and Natural Language Processing (3) ai

The goal of this practicum is to integrate methods from natural language processing with social psychological perspectives on race to build practical systems that address significant societal issues. Readings will be drawn broadly from across the social sciences and computer science. Students will work with large, complex datasets and participate in research involving community partnerships relevant to race and natural language processing.

CS 329S: Machine Learning Systems Design (34) ai

This project-based course covers the iterative process for designing, developing, and deploying machine learning systems. It focuses on systems that require massive datasets and compute resources, such as large neural networks. Students will learn about data management, data engineering, approaches to model selection, training, scaling, how to continually monitor and deploy changes to ML systems, as well as the human side of ML projects. In the process, students will learn about important issues including privacy, fairness, and security.

CS 329T: Trustworthy Machine Learning (3) ai

This course will provide an introduction to state-of-the-art ML methods designed to make AI more trustworthy. The course focuses on four concepts: explanations, fairness, privacy, and robustness. We first discuss how to explain and interpret ML model outputs and inner workings. Then, we examine how bias and unfairness can arise in ML models and learn strategies to mitigate this problem. Next, we look at differential privacy and membership inference in the context of models leaking sensitive information when they are not supposed to. Finally, we look at adversarial attacks and methods for imparting robustness against adversarial manipulation.

CS 329X: Human Centered NLP (34) ai

CS 330: Deep Multi-task and Meta Learning (3) ai

While deep learning has achieved remarkable success in supervised and reinforcement learning problems, such as image classification, speech recognition, and game playing, these models are, to a large degree, specialized for the single task they are trained for. This course will cover the setting where there are multiple tasks to be solved, and study how the structure arising from multiple tasks can be leveraged to learn more efficiently or effectively. This includes: goal-conditioned reinforcement learning techniques that leverage the structure of the provided goal space to learn many tasks significantly faster; meta-learning methods that aim to learn efficient learning algorithms that can learn new tasks quickly; curriculum and lifelong learning, where the problem requires learning a sequence of tasks, leveraging their shared structure to enable knowledge transfer. This is a graduate-level course. By the end of the course, students should be able to understand and implement the state-of-the-art multi-task learning algorithms and be ready to conduct research on these topics.

CS 331B: Interactive Simulation for Robot Learning (3) ai

This course provides a research survey of advanced methods for robot learning in simulation, analyzing the simulation techniques and recent research results enabled by advances in physics and virtual sensing simulation. The course covers two main components: agent-environment interactions and domains for multi-agent and human-robot interaction. First, we cover agent-environment interactions by studying novel simulation environments for robotics, imitation and reinforcement learning methods, simulation for navigation and manipulation and `sim2real' techniques. In the second part, we explore models and algorithms for simulation and robot learning in multi-agent domains and human-robot interaction, studying the principles of learning for interactive tasks in which each agent collaborates to accomplish tasks. The topics include domains of social navigation, human-robot collaborative manipulation and multi-agent settings.nnThis a project-based seminar class. Projects will leverage the state-of-the-art simulation environment iGibson, in which students will develop simulations to explore learning and planning methods for diverse domains. We will provide a list of suggested projects but students might also propose an original idea. The course will cover a set of research papers with presentations by students. This is a research field in rapid transformation with exciting research lines. The goal of the class is to provide practical experience and understanding of the main research lines to enable students to conduct innovative research in this field.

CS 332: Advanced Survey of Reinforcement Learning (3) ai

This class will provide a core overview of essential topics and new research frontiers in reinforcement learning. Planned topics include: model free and model based reinforcement learning, policy search, Monte Carlo Tree Search planning methods, off policy evaluation, exploration, imitation learning, temporal abstraction/hierarchical approaches, safety and risk sensitivity, human-in-the-loop RL, inverse reinforcement learning, learning to communicate, and insights from human learning. Students are expected to create an original research paper on a related topic.

CS 333: Algorithms for Interactive Robotics (34) ai

AI agents need to collaborate and interact with humans in many different settings such as bots operating on social media and crowdsourcing platforms, AI assistants brokering transactions on electronic marketplaces, autonomous vehicles driving alongside humans, or robots interacting with and assisting humans in homes. Our goal in this class is to learn about and design algorithms that enable robots and AI agents to reason about their actions, interact with one another, the humans, and the environment they live in, as well as plan safe strategies that humans can trust and rely on. This is a project-based graduate course that studies algorithms in robotics, machine learning, and control theory, which can improve the state-of-the-art human-AI systems.

CS 336: Language Modeling from Scratch (35) ai

Language models serve as the cornerstone of modern natural language processing (NLP) applications and open up a new paradigm of having a single general purpose system address a range of downstream tasks. As the field of artificial intelligence (AI), machine learning (ML), and NLP continues to grow, possessing a deep understanding of language models becomes essential for scientists and engineers alike. This course is designed to provide students with a comprehensive understanding of language models by walking them through the entire process of developing their own. Drawing inspiration from operating systems courses that create an entire operating system from scratch, we will lead students through every aspect of language model creation, including data collection and cleansing for pre-training, transformer model construction, model training, and evaluation before deployment.

CS 337: AI-Assisted Care (MED 277) (14) ai

AI has been advancing quickly, with its impact everywhere. In healthcare, innovation in AI could help transforming of our healthcare system. This course offers a diverse set of research projects focusing on cutting edge computer vision and machine learning technologies to solve some of healthcare's most important problems. The teaching team and teaching assistants will work closely with students on research projects in this area. Research projects include Care for Senior at Senior Home, Surgical Quality Analysis, AI Assisted Parenting, Burn Analysis & Assessment and more. AI areas include Video Understanding, Image Classification, Object Detection, Segmentation, Action Recognition, Deep Learning, Reinforcement Learning, HCI and more. The course is open to students in both school of medicine and school of engineering.

CS 338: Physical Human Robot Interaction (3) ai

Robotics researchers and futurists have long dreamed of robots that can serve as assistants or caregivers. One important research area to develop such robots in the immediate future is Physical Human-Robot Interaction (pHRI). Assistive robots have the potential to provide adaptable and intelligent assistance to people in need, but developing such a robot is challenging because the robot needs to coordinate its motion with human, often through physical contacts. Reliable mechanical and control methods need to be developed in consideration of actively participating humans, while safety and dependability issues have to be addressed to successfully introduce robots in everyday environments. In this hands-on project-based course, students will learn about future opportunities and present realities for autonomous robots that provide physical assistance to humans. Students will also gain experience with key technologies for the creation of autonomous robots, including perception, action, human-robot interaction, and learning.

CS 339H: Human-Computer Interaction and AI/ML (3) humans

Understanding the human side of AI/ML based systems requires understanding both how the system-side AI works, but also how people think about, understand, and use AI tools and systems. This course will cover how what AI components and systems currently exits, along with how mental models and user models are made. These models lead to user expectations of AI systems are formed, and ultimately to design guidelines to avoid disappointing end-users by creating unintelligible AI tools that are based on a cryptic depiction of how things work. We'll also cover the ethics of AI data collection and model building, as well as how to build fair systems.

CS 339N: Machine Learning Methods for Neural Data Analysis (3) ai

With modern high-density electrodes and optical imaging techniques, neuroscientists routinely measure the activity of hundreds, if not thousands, of cells simultaneously. Coupled with high-resolution behavioral measurements, genetic sequencing, and connectomics, these datasets offer unprecedented opportunities to learn how neural circuits function. This course will study statistical machine learning methods for analysing such datasets, including: spike sorting, calcium deconvolution, and voltage smoothing techniques for extracting relevant signals from raw data; markerless tracking methods for estimating animal pose in behavioral videos; network models for connectomics and fMRI data; state space models for analysis of high-dimensional neural and behavioral time-series; point process models of neural spike trains; and deep learning methods for neural encoding and decoding. We will develop the theory behind these models and algorithms and then apply them to real datasets in the homeworks and final project.

CS 339R: Collaborative Robotics (3) ai

This course focuses on how robots can be effective teammates with other robots and human partners. Concepts and tools will be reviewed for characterizing task objectives, robot perception and control, teammate behavioral modeling, inter-agent communication, and team consensus. We will consider the application of these tools to robot collaborators, wearable robotics, and the latest applications in the relevant literature. This will be a project-based graduate course, with the implementation of algorithms in either python or C++.

CS 340LX: Advanced Operating System Lab: Accelerated (II) (2) sys

This is an implementation-heavy, lab-based class that continues the topics from CS 240LX. The labs will be more specialized, with an emphasis on research-worthy topics and techniques. The class format will follow CS 240LX: two labs, twice a week, along with a set of research papers for context. Enrollment requires instructor permission.

CS 340R: Rusty Systems (3) pls

Language shapes thought; for 40 years, software systems and some of their research challenges have been defined by the C language. In the past 5 years, this has begun to change, with new languages (Rust, Go, coq) becoming competitors to C in large classes of systems. CS 340R is a project-centric course that examines how the Rust programming language changes software systems, solving some problems while presenting new ones. This course seeks to ask and start to answer a simple question: 'What are the most important open research challenges for software systems written in Rust?'

CS 342: Building for Digital Health (34) sys

This project-based course will provide a comprehensive overview of key requirements in the design and full-stack implementation of a digital health research application. Several pre-vetted and approved projects from the Stanford School of Medicine will be available for students to select from and build. Student teams learn about all necessary approval processes to deploy a digital health solution (data privacy clearance/I RB approval, etc.) and be guided in the development of front-end and back-end infrastructure using best practices. The final project will be the presentation and deployment of a fully approved digital health research application.

CS 343D: Domain-Specific Programming Models and Compilers (3) pls

This class will cover the principles and practices of domain-specific programming models and compilers for dense and sparse applications in scientific computing, data science, and machine learning. We will study programming models from the recent literature, categorize them, and discuss their properties. We will also discuss promising directions for their compilation, including the separation of algorithm, schedule, and data representation, polyhedral compilation versus rewrite rules, and sparse iteration theory.

CS 343S: Domain-Specific Language Design Studio (3) pls

This is a design-studio course for the creation of domain-specific languages (DSLs). We will start with lectures teaching fundamental skills for designing and implementing DSLs, followed by a long term project designing and implementing a DSL of the student's choice. The course will particularly emphasize the role that languages can play in tasks that we do not usually think of as programming, such as DSLs for knitting patterns or geometric constructions.

CS 344: Topics in Computer Networks (3) sys

This class could also be called 'Build an Internet Router': Students work in teams of two to build a fully functioning Internet router, gaining hands-on experience building the hardware and software of a high-performance network system. Students design the control plane in C on a linux host and design the data plane in the new P4 language on both a software switch and a high-speed hardware switch (e.g., Intel Tofino). For the midterm milestone, teams must demonstrate that their routers can interoperate with the other teams by building a small scale datacenter topology. In the final 3-4 weeks of the class, teams will participate in an open-ended design challenge.

CS 347: Human-Computer Interaction: Foundations and Frontiers (34) humans

How will the future of human-computer interaction evolve? This course equips students with the major animating theories of human-computer interaction, and connects those theories to modern innovations in research. Major theories are drawn from interaction (e.g., tangible and ubiquitous computing), social computing (e.g., Johansen matrix), and design (e.g., reflective practitioner, wicked problems), and span domains such as AI+HCI (e.g., mixed initiative interaction), accessibility (e.g., ability based design), and interface software tools (e.g., threshold/ceiling diagrams). Students read and comment on multiple research papers per week, and perform a quarter-long research project.

CS 348A: Computer Graphics: Geometric Modeling & Processing (3) graphics

The mathematical tools needed for the geometrical aspects of computer graphics and especially for modeling smooth shapes. The course covers classical computer-aided design, geometry processing, and data-driven approaches for shape generation. Fundamentals: homogeneous coordinates and transformation. Theory of parametric and implicit curve and surface models: polar forms, Bézier arcs and de Casteljau subdivision, continuity constraints, B-splines, tensor product, and triangular patch surfaces. Subdivision surfaces and multi-resolution representations of geometry. Surface reconstruction from scattered data points. Geometry processing on meshes, including simplification and parametrization. Deep neural generative models for 3D geometry: parametric and implicit approaches, VAEs and GANs.

CS 348B: Computer Graphics: Image Synthesis Techniques (34) graphics

Intermediate level, emphasizing high-quality image synthesis algorithms and systems issues in rendering. Topics include: Reyes and advanced rasterization, including motion blur and depth of field; ray tracing and physically based rendering; Monte Carlo algorithms for rendering, including direct illumination and global illumination; path tracing and photon mapping; surface reflection and light source models; volume rendering and subsurface scattering; SIMD and multi-core parallelism for rendering. Written assignments and programming projects.

CS 348C: Computer Graphics: Animation and Simulation (3) graphics

Core mathematics and methods for computer animation and motion simulation. Traditional animation techniques. Physics-based simulation methods for modeling shape and motion: particle systems, constraints, rigid bodies, deformable models, collisions and contact, fluids, and fracture. Animating natural phenomena. Methods for animating virtual characters and crowds. Additional topics selected from data-driven animation methods, realism and perception, animation systems, motion control, real-time and interactive methods, and multi-sensory feedback.

CS 348E: Character Animation: Modeling, Simulation, and Control of Human Motion (3) graphics

This course introduces technologies and mathematical tools for simulating, modeling, and controlling human/animal movements. Students will be exposed to integrated knowledge and techniques across computer graphics, robotics, machine learning and biomechanics. The topics include numerical integration, 3D character modeling, keyframe animation, skinning/rigging, multi-body dynamics, human kinematics, muscle dynamics, trajectory optimization, learning policies for motor skills, and motion capture. Students who successfully complete this course will be able to use and modify physics simulator for character animation or robotic applications, to design/train control policies for locomotion or manipulation tasks on virtual agents, and to leverage motion capture data for synthesizing realistic virtual humans. The evaluation of this course is based on three assignments and an open-ended research project.

CS 348I: Computer Graphics in the Era of AI (34) graphics

This course introduces deep learning methods and AI technologies applied to four main areas of Computer Graphics: rendering, geometry, animation, and imaging. We will study a wide range of problems on content creation for images, shapes, and animations, recently advanced by deep learning techniques. For each problem, we will understand its conventional solutions, study the state-of-the-art learning-based approaches, and critically evaluate their results as well as the impacts to researchers and practitioners in Computer Graphics. The topics include differentiable rendering/neural rendering, BRDF estimation, texture synthesis, denoising, procedural modeling, view synthesis, colorization, style transfer, motion synthesis, differentiable physics simulation, and reinforcement learning. Through programming projects and homework, students who successfully complete this course will be able to use neural rendering algorithms for image manipulation, apply neural procedural modeling for shape and scene synthesis, exploit data-driven methods for simulating physical phenomena, and implement policy learning algorithms for creating character animation.

CS 348K: Visual Computing Systems (34) graphics

Visual computing tasks such as computational photography, image/video understanding, and real-time 3D graphics are key responsibilities of modern computer systems ranging from sensor-rich smart phones, autonomous robots, and large data centers. These workloads demand exceptional system efficiency and this course examines the key ideas, techniques, and challenges associated with the design of parallel, heterogeneous systems that execute and accelerate visual computing applications. This course is intended for graduate and advanced undergraduate-level students interested in architecting efficient graphics, image processing, and computer vision systems (both new hardware architectures and domain-optimized programming frameworks) and for students in graphics, vision, and ML that seek to understand throughput computing concepts so they can develop scalable algorithms for these platforms. Students will perform daily research paper readings, complete simple programming assignments, and compete a self-selected term project.

CS 348N: Neural Models for 3D Geometry (3) graphics

Generation of high-quality 3D models and scenes by leveraging machine learning tools and approaches. Survey of geometry representations. Public 3D object and scene data sets. Neural architectures for geometry, including deep architectures for point clouds and meshes. Generative models for 3D: autoencoders, GANs, neural implicits, neural ODEs, autoregressive models. Conditional generation based on images or partial geometry. Variation generation. Evaluation metrics for content generation. Use of synthetic data in ML training pipelines.

CS 349D: Cloud Computing Technology (3) sys

The largest change in the computer industry over the past twenty years has arguably been the emergence of cloud computing: organizations are increasingly developing their workloads to be cloud native, using global-scale compute, storage, and communication services that were simply not possible with private infrastructure. This research seminar covers the latest technical advances and open issues in cloud computing, including cloud infrastructure for AI inference and training, cloud databases and data lakes, resource management, serverless computing, confidential computing, multi-cloud computing, and AI for cloud management. Students will propose and develop an original research project in cloud computing.

CS 349F: Technology for Financial Systems (2) sys

Financial systems have spurred technological innovation and, in turn, are driven by cutting-edge technological developments. This course explores the synergy. Students will learn from faculty and industry experts how to build faster and fairer financial systems. Topics include network infrastructure: data center fabrics, ultra-low latency trading systems; cloud computing infrastructure: building large-scale risk computation platforms using virtual machines, containers and serverless computing. A particular focus will be on challenges and opportunities presented by cloud-native financial exchanges: the course will provide such an exchange and student groups will write programs for high-frequency and algorithmic trading.

CS 349G: Selected Reading of Ph.D. Dissertations (3) special

Detailed reading of 5 selected Ph.D. dissertations within a field of computer science. For undergraduates, the course is an introduction to advanced foundational concepts within a field as well as an in-depth look at detailed research. For graduate students, the course focuses on historical reading as well as an opportunity to read dissertations and discuss their strengths and weaknesses. Both groups of students discuss historical context, how ideas succeeded or did not and why, and how they manifest in modern technology. The discussion of each dissertation completes with a guest lecture by its author. The selected dissertations change with each offering but are always from a coherent time period and topic area.

CS 349H: Software Techniques for Emerging Hardware Platforms (EE 292Y) (3) sys

Research seminar on software techniques for emerging computational substrates with guest lectures from hardware designers from research and industry. This seminar explores the benefits of novel hardware technologies, the challenges gating broad adoption of these technologies, and how software techniques can help mitigate these challenges and improve the usability of these hardware platforms. Note that the computational substrates discussed vary depending on the semester. Topics covered include: In-memory computing platforms, dynamical system-solving mixed-signal devices, flexible and bendable electronics, neuromorphic computers, intermittent computing platforms, ReRAMs, DNA-based storage, and optical computing platforms.

CS 349M: Machine Learning for Software Engineering (34) sys

In recent years, tools based on machine learning have become increasingly prevalent in the software engineering field. The ubiquity of machine learning is an important factor, but just as important is the availability of software engineering data: there are billions of lines of code available in public repositories (e.g. on GitHub), there is the change history of that code, there are discussion fora (e.g. Stack Overflow) that contain a wealth of information for developers, companies have access to telemetry on their apps from millions of users, and so on. The scale of software engineering data has permitted machine learning and statistical approaches to imagine tools that are beyond the capabilities of traditional, semantics-based approaches. In this graduate seminar, students will learn the various ways in which code and related artifacts can be treated as data, and how various developer tools can be built by applying machine learning over this data. The course will consist of discussion of a selection of research papers, as well as a hands-on project that can be done in small groups.

CS 349T: Project Lab: Video and Audio Technology for Live Theater in the Age of COVID (3) sys

This class is part of a multi-disciplinary collaboration between researchers in the CS, EE, and TAPS departments to design and develop a system to host a live theatrical production that will take place over the Internet in the winter quarter. The performing arts have been greatly affected by a transition to theater over Zoom and its competitors, none of which are great at delivering low-latency audio to actors, or high-quality audio and video to the audience, or feedback from the audience back to actors. These are big technical challenges. During the fall, we'll build a system that improves on current systems in certain areas: audio quality and latency over spotty Internet connections, video quality and realistic composited scenes with multiple actors, audience feedback, and perhaps digital puppetry. Students will learn to be part of a deadline-driven software development effort working to meet the needs of a theater director and creative specialists -- while communicating the effect of resource limits and constraints to a nontechnical audience. This is an experimental hands-on laboratory class, and our direction may shift as the creative needs of the theatrical production evolve. Based on the success of class projects and subsequent needs, some students may be invited to continue in the winter term with a research appointment (for pay or credit) to operate the system you have built and instruct actors and creative professionals how to work with the system through rehearsals and the final performance before spring break.

CS 350: Secure Compilation (3) pls

This course explores the field of secure compilation, which sits at the intersection between security and programming languages. The course covers the following topics: threat models for secure compilers, formal criteria for secure compilers to adhere to, security relevance of secure compilation criteria, security architectures employed to achieve secure compilation, proof techniques for secure compilation with a focus on backtranslation.

CS 351: Open Problems in Coding Theory (3) math

Coding theory is the study of how to encode data to protect it from noise. Coding theory touches CS, EE, math, and many other areas, and there are exciting open problems at all of these frontiers. In this class, we will explore these open problems by reading recent research papers and thinking about some open problems together. Required work will involve reading and presenting research papers, as well as working in small groups at these open problems and presenting progress. (Solving an open problem is not required!) Topics will depend on student interest and may include locality, coded computation, index coding, interactive communication, and group testing.

CS 352B: Blockchain Governance (3) sys

This course offers an overview of blockchain governance and Decentralized Autonomous Organizations (DAOs), with topics including DAO tooling, on-chain and off-chain voting, delegation, constitutional design, alternative governance mechanisms, identity, and privacy. We will cover these topics and others from technical, social science, and legal perspectives, and we will include a range of guests from the web3 space as well as several speakers who are on the frontiers of DAO research.

CS 353: Seminar on Logic & Formal Philosophy (24) theory

Contemporary work. May be repeated a total of three times for credit.

CS 354: Topics in Intractability: Unfulfilled Algorithmic Fantasies (3) theory

Over the past 45 years, understanding NP-hardness has been an amazingly useful tool for algorithm designers. This course will expose students to additional ways to reason about obstacles for designing efficient algorithms. Topics will include unconditional lower bounds (query- and communication-complexity), total problems, Unique Games, average-case complexity, and fine-grained complexity.

CS 355: Advanced Topics in Cryptography (3) pls

Topics: Pseudo randomness, multiparty computation, pairing-based and lattice-based cryptography, zero knowledge protocols, and new encryption and integrity paradigms. May be repeated for credit.

CS 356: Topics in Computer and Network Security (3) sys

Research seminar covering foundational work and current topics in computer and network security. Students will read and discuss published research papers as well as complete an original research project in small groups. Open to Ph.D. and masters students as well as advanced undergraduate students.

CS 357S: Formal Methods for Computer Systems (3) pls

The complexity of modern computer systems requires rigorous and systematic verification/validation techniques to evaluate their ability to correctly and securely support application programs. To this end, a growing body of work in both industry and academia leverages formal methods techniques to solve computer systems challenges. This course is a research seminar that will cover foundational work and current topics in the application of formal methods-style techniques to reliable and secure computer systems design.

CS 358A: Programming Language Foundations (3) pls

This course introduces advanced formal systems and programming languages as well as techniques to reason formally about them. Possible systems of study include: the lambda calculus, System F, the Pi and Spi calculi, simply-typed languages, security type systems for non-interference, robust safety, linear types, ownership types, session types, logical relations and semantic models etc.

CS 359A: Research Seminar in Complexity Theory (3) theory

A research seminar on computational complexity theory. The focus of this year's offering will be on concrete complexity, a major strand of research in modern complexity theory. We will cover fundamental techniques and major results concerning basic models of computation such as circuits, decision trees, branching problems, and halfspaces.

CS 359D: Quantum Complexity Theory (3) theory

Introduction to quantum complexity theory. Topics include: the class BQP and its relation to other complexity classes; quantum query and communication complexity; quantum proof systems, Hamiltonian complexity, and the quantum PCP conjecture; the complexity & verification of quantum sampling experiments; and quantum cryptography.

CS 360: Simplicity and Complexity in Economic Theory (35) theory

Technology has enabled the emergence of economic systems of formerly inconceivable complexity. Nevertheless, some technology-related economic problems are so complex that either supercomputers cannot solve them in a reasonable time, or they are too complex for humans to comprehend. Thus, modern economic designs must still be simple enough for humans to understand, and must address computationally complex problems in an efficient fashion. This topics course explores simplicity and complexity in economics, primarily via theoretical models. We will focus on recent advances. Key topics include (but are not limited to) resource allocation in complex environments, communication complexity and information aggregation in markets, robust mechanisms, dynamic matching theory, influence maximization in networks, and the design of simple (user-friendly) mechanisms. Some applications include paired kidney exchange, auctions for electricity and for radio spectrum, ride-sharing platforms, and the diffusion of information.

CS 361: Engineering Design Optimization (34) algs

Design of engineering systems within a formal optimization framework. This course covers the mathematical and algorithmic fundamentals of optimization, including derivative and derivative-free approaches for both linear and non-linear problems, with an emphasis on multidisciplinary design optimization. Topics will also include quantitative methodologies for addressing various challenges, such as accommodating multiple objectives, automating differentiation, handling uncertainty in evaluations, selecting design points for experimentation, and principled methods for optimization when evaluations are expensive. Applications range from the design of aircraft to automated vehicles.

CS 362: Research in AI Alignment (3) ai

In this course we will explore the current state of research in the field of AI alignment, which seeks to bring increasingly intelligent AI systems in line with human values and interests. The purpose of this course is to encourage the development of new ideas in this field, where a dominant paradigm has not yet been established. The format will be weekly lectures in which speakers present their current research approaches. The assignment structure will be slightly unusual: each week students will have a choice between a problem set and a short research assignment based on the weekly guest speaker's research area. For the research assignment, students will start with the abstract of a relevant AI alignment paper or blog post and create a blog post or Github repository describing how they would continue the paper. The final weekly assignment will be an extension of one of the previous weeks' work. Therefore this course requires research experience, preferably using mathematical and programming tools (e.g. Python, PyTorch, calculus), and is a graduate level course, open to advanced undergraduates.

CS 366: Computational Social Choice (3) algs

An in-depth treatment of algorithmic and game-theoretic issues in social choice. Topics include common voting rules and impossibility results; ordinal vs cardinal voting; market approaches to large scale decision making; voting in complex elections, including multi-winner elections and participatory budgeting; protocols for large scale negotiation and deliberation; fairness in societal decision making;nalgorithmic approaches to governance of modern distributed systems such as blockchains and community-mediated social networks; opinion dynamics and polarization.

CS 368: Algorithmic Techniques for Big Data (3) algs

Designing algorithms for efficient processing of large data sets poses unique challenges. This course will discuss algorithmic paradigms that have been developed to efficiently process data sets that are much larger than available memory. We will cover streaming algorithms and sketching methods that produce compact datanstructures, dimension reduction methods that preserve geometric structure, efficient algorithms for numerical linear algebra, graph sparsification methods, as well as impossibility results for these techniques.

CS 369O: Optimization Algorithms (3) algs

Fundamental theory for solving continuous optimization problems with provable efficiency guarantees. Coverage of both canonical optimization methods and techniques, e.g. gradient descent, mirror descent, stochastic methods, acceleration, higher-order methods, etc. and canonical optimization problems, critical point computation for non-convex functions, smooth-convex function minimization, regression, linear programming, etc. Focus on provable rates for solving broad classes of prevalent problems including both classic problems and those motivated by large-scale computational concerns. Discussion of computational ramifications, fundamental information-theoretic limits, and problem structure.

CS 369Z: Dynamic Data Structures for Graphs (3) algs

With the increase of huge, dynamically changing data sets there is a raising need for dynamic data structures to represent and process them. This course will present the algorithmic techniques that have been developed for dynamic data structures for graphs and for point sets.

CS 371: Computational Biology in Four Dimensions (3) ai

Cutting-edge research on computational techniques for investigating and designing the three-dimensional structure and dynamics of biomolecules, cells, and everything in between. These techniques, which draw on approaches ranging from physics-based simulation to machine learning, play an increasingly important role in drug discovery, medicine, bioengineering, and molecular biology. Course is devoted primarily to reading, presentation, discussion, and critique of papers describing important recent research developments.

CS 372: Artificial Intelligence for Precision Medicine and Psychiatric Disorders (3) ai

Artificial intelligence, specifically deep learning, stands out as one of the most transformative technologies of the past decade. AI can already outperform humans in several computer vision and natural language processing tasks. However, we still face some of the same limitations and obstacles that led to the demise of the first AI boom phase five decades ago. This research-oriented course will first review and reveal the limitations (e.g., iid assumption on training and testing data, voluminous training data requirement, and lacking interpretability) of some widely used AI algorithms, including convolutional neural networks (CNNs), transformers, reinforcement learning, and generative adversarial networks (GANs). To address these limitations, we will then explore topics including transfer learning for remedying data scarcity, knowledge-guided multimodal learning for improving data diversity, out of distribution generalization, attention mechanisms for enabling Interpretability, meta learning, and privacy-preserving training data management. The course will be taught through a combination of lecture and project sessions. Lectures on specialized AI applications (e.g., cancer/depression diagnosis and treatment, AI/VR for surgery, and health education) will feature guest speakers from academia and industry. Students will be assigned to work on an extensive project that is relevant to their fields of study (e.g., CS, Medicine, and Data Science). Projects may involve conducting literature surveys, formulating ideas, and implementing these ideas. Example project topics are but not limited to 1) knowledge guided GANs for improving training data diversity, 2) disease diagnosis via multimodal symptom checking, and 3) fake and biased news/information detection.

CS 375: Large-Scale Neural Network Modeling for Neuroscience (3) ai

The last ten years has seen a watershed in the development of large-scale neural networks in artificial intelligence. At the same time, computational neuroscientists have discovered a surprisingly robust mapping between the internal components of these networks and real neural structures in the human brain. In this class we will discuss a panoply of examples of such 'convergent man-machine evolution', including: feedforward models of sensory systems (vision, audition, somatosensation); recurrent neural networks for dynamics and motor control; integrated models of attention, memory, and navigation; transformer models of language areas; self-supervised models of learning; and deep RL models of decision and planning. We will also delve into the methods and metrics for comparing such models to real-world neural data, and address how unsolved open problems in AI (that you can work on!) will drive forward novel neural models. Some meaningful background in modern neural networks is highly advised (e.g. CS 229, CS 230, CS 231n, CS 234, CS 236, CS 330), but formal preparation in cognitive science or neuroscience is not needed (we will provide this).

CS 377E: Designing Solutions to Global Grand Challenges (34) humans

In this course we creatively apply information technologies to collectively attack Global Grand Challenges (e.g., global warming, rising healthcare costs and declining access, and ensuring quality education for all). Interdisciplinary student teams will carry out need-finding within a target domain, followed by brainstorming to propose a quarter long project. Teams will spend the rest of the quarter applying user-centered design methods to rapidly iterate through design, prototyping, and testing of their solutions. This course will interweave a weekly lecture with a weekly studio session where students apply the techniques hands-on in a small-scale, supportive environment. Note: Cardinal Course certified by the Haas Center for Public Service

CS 377G: Designing Serious Games (34) humans

Over the last few years we have seen the rise of 'serious games' to promote understanding of complex social and ecological challenges, and to create passion for solving them. This project-based course provides an introduction to game design principals while applying them to games that teach. Run as a hands-on studio class, students will design and prototype games for social change and civic engagement. We will learn the fundamentals of games design via lecture and extensive reading in order to make effective games to explore issues facing society today. The course culminates in an end-of- quarter open house to showcase our games.

CS 377Q: Designing for Accessibility (ME 214) (34) humans

Designing for accessibility is a valuable and important skill in the UX community. As businesses are becomeing more aware of the needs and scope of people with some form of disability, the benefits of universal design, where designing for accessibility ends up benefiting everyone, are becoming more apparent. This class introduces fundamental Human Computer Interaction (HCI) concepts and skills in designing for accessibility through individual assignments. Student projects will identify an accessibility need, prototype a design solution, and conduct a user study with a person with a disability. This class focuses on the accessibility of UX with computers, mobile phones, VR, and has a design class prerequisite (e.g., CS 147, ME115A).

CS 377U: Understanding Users (34) humans

This project-based class focuses on understanding the use of technology in the world. Students will learn generative and evaluative research methods to explore how systems are appropriated into everyday life in a quarter-long project where they design, implement and evaluate a novel mobile application. Quantitative (e.g. A/B testing, instrumentation, analytics, surveys) and qualitative (e.g. diary studies, contextual inquiry, ethnography) methods and their combination will be covered along with practical experience applying these methods in their project.

CS 379C: Computational Models of the Neocortex (3) ai

This class focuses on building agents that achieve human-level performance in specialized technical domains and are adept at collaborating with humans using natural language. We draw upon research in cognitive and systems neuroscience to take advantage of what is known about how humans communicate and solve problems in order to design advanced artificial neural network architectures. For more detail on invited speakers, schedule of talks and project milestones, see here: https://web.stanford.edu/class/CS 379c/class_messages_listing/curriculum/

CS 381: Sensorimotor Learning for Embodied Agents (EE 381) (3) ai

This is an advanced course that will focus on modern machine learning algorithms for autonomous robots as an embodied intelligent agent. It covers advanced topics that center around 1. what is embodied AI and how it differs from internet AI, 2. how embodied agents perceive their environment from raw sensory data and make decisions, and 3. continually adapt to the physical world through both hardware and software improvements. By the end of the course, we hope to prepare you for conducting research in this area, knowing how to formulate the problem, design the algorithm, critically validate the idea through experimental designs and finally clearly present and communicate the findings. Students are expected to read, present, and debate the latest research papers on embodied AI, as well as obtain hands-on experience through the course projects.

CS 384: Seminar on Ethical and Social Issues in Natural Language Processing (LINGUIST 287) (34) impact

Seminar covering issues in natural language processing related to ethical and social issues and the overall impact of these algorithms on people and society. Topics include: bias in data and models, privacy and computational profiling, measuring civility and toxicity online, computational propaganda, manipulation and framing, fairness/equity, power, recommendations and filter bubbles, applications to social good, and philosophical foundations of ethical investigation.

CS 390A: Curricular Practical Training (1)

Educational opportunities in high technology research and development labs in the computing industry. Qualified computer science students engage in internship work and integrate that work into their academic program. Students register under their faculty advisor during the quarter they are employed and complete a research report outlining their work activity, problems investigated, results, and follow-on projects they expect to perform.

CS 390B: Curricular Practical Training (1)

CS 390C: Curricular Practical Training (1)

CS 398: Computational Education (4) ai

This course covers cutting-edge education algorithms used to model students, assess learning, and design widely deployable tools for open access education. The goal of the course is for you to be ready to lead your own computation education research project. Topics include knowledge tracing, generative grading, teachable agents, and challenges and opportunities implementing computational education in diverse contexts around the world. The course will consist of group and individual work and encourages creativity.

CS 399: Independent Project (19) special

Letter grade only. This course is for masters students only. Undergraduate students should enroll in CS 199; PhD students should enroll in CS 499. Letter grade; if not appropriate, enroll in CS 399P. Register using the section number associated with the instructor.

CS 399P: Independent Project (19) special

Graded satisfactory/no credit. This course is for masters students only. Undergraduate students should enroll in CS 199; PhD students should enroll in CS 499. S/NC only; if not appropriate, enroll in CS 399. Register using the section number associated with the instructor.

CS 407: Lytics Seminar (EDUC 407) (14) ai

Students will learn to design technology mediated learning environments for adult learners, conduct research in those environments, and learn from prior EdTech failures. Grounded in various theoretical frameworks that inform the design of learning environments, the course explores how people learn and the evidence of learning that can be collected and modeled in online environments in real world contexts. The course also examines specific case studies of failed EdTech ventures to identify patterns and causes of failure. Throughout the course we will consider ethical issues related to design and research in human learning. Overall, this course will provide students with a foundation in learning theory and the skills and knowledge needed to design, implement, and evaluate effective technology mediated learning environments.

CS 421: Designing AI to Cultivate Human Well-Being (2) ai

Artificial Intelligence (AI) has the potential to drive us towards a better future for all of humanity, but it also comes with significant risks and challenges. At its best, AI can help humans mitigate climate change, diagnose and treat diseases more effectively, enhance learning, and improve access to capital throughout the world. But it also has the potential to exacerbate human biases, destroy trust in information flow, displace entire industries, and amplify inequality throughout the world. We have arrived at a pivotal moment in the development of the technology in which we must establish a foundation for how we will design AI to capture the positive potential and mitigate the negative risks. To do this, building AI must be an inclusive, interactive, and introspective process guided by an affirmative vision of a beneficial AI-future. The goal of this interdisciplinary class is to bridge the gap between technological and societal objectives: How do we design AI to promote human well-being? The ultimate aim is to provide tools and frameworks to build a more harmonious human society based on cooperation toward a shared vision. Thus, students are trained in basic science to understand what brings about the conditions for human flourishing and will create meaningful AI technologies that aligns with the PACE framework: 1) has a clear and meaningful purpose, 2) augments human dignity and autonomy, 3) creates a feeling of inclusivity and collaboration, 4) creates shared prosperity and a sense of forward movement (excellence). Toward this end, students work in interdisciplinary teams on a final project and propose a solution that tackles a significant societal challenge by leveraging technology and frameworks on human thriving.

CS 422: Interactive and Embodied Learning (EDUC 234A) (315) ai

Most successful machine learning algorithms of today use either carefully curated, human-labeled datasets, or large amounts of experience aimed at achieving well-defined goals within specific environments. In contrast, people learn through their agency: they interact with their environments, exploring and building complex mental models of their world so as to be able to flexibly adapt to a wide variety of tasks. One crucial next direction in artificial intelligence is to create artificial agents that learn in this flexible and robust way. Students will read and take turns presenting current works, and they will produce a proposal of a feasible next research direction.

CS 428A: Probabilistic models of cognition: Reasoning and Learning (PSYCH 220A) (3) ai

How can we understand intelligent behavior as computation? This course introduces probabilistic programming as a tool for cognitive modeling. We will use probabilistic generative models to explain aspects of human and artificial cognition. Topics will be drawn from causal and social reasoning, concept learning, and hierarchical abstraction.

CS 428B: Probabilistic Models of Cognition: Language (LINGUIST 238B, PSYCH 220B) (3) ai

How can we understand natural language use in computational terms? This course surveys probabilistic models for natural language semantics and pragmatics. It begins with an introduction to the Rational Speech Acts framework for modeling pragmatics as social reasoning. It then explores a variety of phenomena in language meaning and usage. Probabilistic programming will be used as a precise and practical way to express models.

CS 431: High-level Vision: From Neurons to Deep Neural Networks (13) ai

Interdisciplinary seminar focusing on understanding how computations in the brain enable rapid and efficient object perception. Covers topics from multiple perspectives drawing on recent research in Psychology, Neuroscience, and Computer Science. Emphasis on discussing recent empirical findings, methods and theoretical debates in the field.

CS 432: Computer Vision for Education and Social Science Research (3) ai

Computer vision -- the study of how to design artificial systems that can perform high-level tasks related to image or video data (e.g. recognizing and locating objects in images and behaviors in videos) -- has seen recent dramatic success. In this course, we seek to give education and social science researchers the know-how needed to apply cutting edge computer vision algorithms in their work as well as an opportunity to workshop applications.

CS 448B: Data Visualization (34) graphics

Techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science. Topics: graphical perception, data and image models, visual encoding, graph and tree layout, color, animation, interaction techniques, automated design. Lectures, reading, and project.

CS 448I: Computational Imaging (3) ai

Digital photography and basic image processing, convolutional neural networks for image processing, denoising, deconvolution, single pixel imaging, inverse problems in imaging, proximal gradient methods, introduction to wave optics, time-of-flight imaging, end-to-end optimization of optics and imaging processing. Emphasis is on applied image processing and solving inverse problems using classic algorithms, formal optimization, and modern artificial intelligence techniques. Students learn to apply material by implementing and investigating image processing algorithms in Python. Term project.

CS 448Z: Physically Based Animation and Sound (34) graphics

Intermediate level, emphasizing physically based simulation techniques for computer animation and synchronized sound synthesis. Topics vary from year to year, but include the simulation of acoustic waves, and integrated approaches to visual and auditory simulation of rigid bodies, deformable solids, collision detection and contact resolution, fracture, fluids and gases, and virtual characters. Students will read and discuss papers, and do programming projects.

CS 468: Topics in Geometric Algorithms: Non-Euclidean Methods in Machine Learning (3) algs

Contents of this course vary with each offering. Past offerings have included geometric matching, surface reconstruction, collision detection, computational topology, differential geometry for computer scientists, computational symmetry and regularity, data-driven shape analysis, and non-Euclidean methods in machine learning.

CS 470: Music and AI (34) ai

How do we make music with artificial intelligence? What does it mean to do so (and is it even a good idea)? How might we design systems that balance machine automation and human interaction? More broadly, how do we want to live with our technologies? Are there - and ought there be - limits to using AI for art? (And what is Art, anyway?) In this 'critical making' course, students will learn practical tools and techniques for AI-mediated music creation, engineer software systems incorporating AI, HCI and Music, and critically reflect on the aesthetic and ethical dimensions of technology.

CS 476A: Music, Computing, Design: The Art of Design (34) graphics

This course explores the artful design of software tools, toys, games, instruments, and experiences. Topics include programming, audiovisual design, strategies for crafting interactive systems, game design, as well as aesthetic and social considerations of shaping technology in our world today. Course work features several programming assignments with an emphasis on critical design feedback, reading responses, and a 'design your own' final project.

CS 498C: Introduction to CSCL: Computer-Supported Collaborative Learning (3) talks

This seminar introduces students to foundational concepts and research on computer-supported collaborative learning (CSCL). It is designed for LSTD doctoral students, LDT masters' students, other GSE graduate students and advanced undergraduates inquiring about theory, research and design of CSCL. CSCL is defined as a triadic structure of collaboration mediated by a computational artefact (participant-artifact-participant). CSCL encompasses two individuals performing a task together in a short time, small or class-sized groups, and students following the same course, digitally interacting.

CS 498D: Design for Learning: Generative AI for Collaborative Learning (3) ai

Would you like to design ways to use generative AI to help humans learn with other humans? In this course, you will develop creative ways to use generative AI to support collaborative learning, also learning more about AI as researchers continue to improve tools like ChatGPT. In creating new learning activities that could be used at Stanford or in other courses, you will build experience with fundamentals of design, including the design abilities of learning from others, navigating ambiguity, synthesizing information, and experimenting rapidly. You will do this by tackling real design challenges presented by our project partners, which include several Stanford programs, while drawing on your own first-hand experience as students. This class is open to all students, undergraduate and graduate, of any discipline. No previous design experience or experience with AI is required. Just a collaborative spirit and hard work.

CS 499: Advanced Reading and Research (115) special

Advanced reading and research for CS PhD students. Register using the section number associated with the instructor. Prerequisite: consent of instructor. This course is for PhD students only. Undergraduate students should enroll in CS 199, masters students should enroll in CS 399.

CS 499P: Advanced Reading and Research (115) special

CS 520: Knowledge Graphs (1) ai

Knowledge graphs have emerged as a compelling abstraction for organizing world's structured knowledge over the internet, capturing relationships among key entities of interest to enterprises, and a way to integrate information extracted from multiple data sources. Knowledge graphs have also started to play a central role in machine learning and natural language processing as a method to incorporate world knowledge, as a target knowledge representation for extracted knowledge, and for explaining what is being learned. This class is a graduate level research seminar and will include lectures on knowledge graph topics (e.g., data models, creation, inference, access) and invited lectures from prominent researchers and industry practitioners. The seminar emphasizes synthesis of AI, database systems and HCI in creating integrated intelligent systems centered around knowledge graphs.

CS 521: Seminar on AI Safety (1) talks

In this seminar, we will focus on the challenges in the design of safe and verified AI-based systems. We will explore some of the major problems in this area from the viewpoint of industry and academia. We plan to have a weekly seminar speaker to discuss issues such as verification of AI systems, reward misalignment and hacking, secure and attack-resilient AI systems, diagnosis and repair, issues regarding policy and ethics, as well as the implications of AI safety in automotive industry.

CS 522: Seminar in Artificial Intelligence in Healthcare (1) talks

Artificial intelligence is poised to make radical changes in healthcare, transforming areas such as diagnosis, genomics, surgical robotics, and drug discovery. In the coming years, artificial intelligence has the potential to lower healthcare costs, identify more effective treatments, and facilitate prevention and early detection of diseases. This class is a seminar series featuring prominent researchers, physicians, entrepreneurs, and venture capitalists, all sharing their thoughts on the future of healthcare. We highly encourage students of all backgrounds to enroll (no AI/healthcare background necessary).

CS 523: Research Seminar in Computer Vision + X (12) talks

With advances in deep learning, computer vision (CV) has been transforming all sorts of domains, including healthcare, human-computer interaction, transportation, art, sustainability, and so much more. In this seminar, we investigate its far-reaching applications, with a different theme chosen as the focus each quarter (e.g. the inaugural quarter was CV + Healthcare; the theme for the quarter will be listed on the class syllabus). Throughout the quarter, we deeply examine these themes in CV + X research through weekly intimate discussions with researchers from academia and industry labs who conduct research at the center of CV and other domains. Each week, students will read and prepare questions and reflections on an assigned paper authored by that week's speaker. We highly encourage students who are interested in taking an interactive, deep dive into CV research literature to apply.

CS 528: Machine Learning Systems Seminar (13) talks

Machine learning is driving exciting changes and progress in computing systems. What does the ubiquity of machine learning mean for how people build and deploy systems and applications? What challenges does industry face when deploying machine learning systems in the real world, and how can new system designs meet those challenges? In this weekly talk series, we will invite speakers working at the frontier of machine learning systems, and focus on how machine learning changes the modern programming stack. Topics will include programming models for ML, infrastructure to support ML applications such as ML Platforms, debugging, parallel computing, and hardware for ML. May be repeated for credit.

CS 529: Robotics and Autonomous Systems Seminar (AA 289) (199) talks

Seminar talks by researchers and industry professionals on topics related to modern robotics and autonomous systems. Broadly, talks will cover robotic design, perception and navigation, planning and control, and learning for complex robotic systems. May be repeated for credit.

CS 547: Human-Computer Interaction Seminar (1) humans

Weekly speakers on human-computer interaction topics. May be repeated for credit.

CS 802: TGR Dissertation (0) capstone

Terminal Graduate Registration (TGR). CS PhD students who have their TGR form approved should register under the section number associated with their faculty advisor.

CS 100BACE: Problem-solving Lab for CS 106B (1) intro

Additional problem solving practice for the introductory CS course CS 106B. Sections are designed to allow students to acquire a deeper understanding of CS and its applications, work collaboratively, and develop a mastery of the material. Limited enrollment, permission of instructor required. Concurrent enrollment in CS 106B required.

MATH 19: Calculus (3) math

Introduction to differential calculus of functions of one variable. Review of elementary functions (including exponentials and logarithms), limits, rates of change, the derivative and its properties, applications of the derivative.

MATH 20: Calculus (3) math

The definite integral, Riemann sums, antiderivatives, the Fundamental Theorem of Calculus. Integration by substitution and by parts. Area between curves, and volume by slices, washers, and shells. Initial-value problems, exponential and logistic models, direction fields, and parametric curves.

MATH 21: Calculus (4) math

This course addresses a variety of topics centered around the theme of 'calculus with infinite processes', largely the content of BC-level AP Calculus that isn't in the AB-level syllabus.

MATH 51: Linear Algebra, Multivariable Calculus, and Modern Applications (5) math

This course provides unified coverage of linear algebra and multivariable differential calculus, and the free course e-text connects the material to many fields. Linear algebra in large dimensions underlies the scientific, data-driven, and computational tasks of the 21st century. The linear algebra portion includes orthogonality, linear independence, matrix algebra, and eigenvalues with applications such as least squares, linear regression, and Markov chains (relevant to population dynamics, molecular chemistry, and PageRank); the singular value decomposition (essential in image compression, topic modeling, and data-intensive work in many fields) is introduced in the final chapter of the e-text. The multivariable calculus portion includes unconstrained optimization via gradients and Hessians (used for energy minimization), constrained optimization (via Lagrange multipliers, crucial in economics), gradient descent and the multivariable Chain Rule (which underlie many machine learning algorithms, such as backpropagation), and Newton's method (an ingredient in GPS and robotics). The course emphasizes computations alongside an intuitive understanding of key ideas. The widespread use of computers makes it important for users of math to understand concepts: novel users of quantitative tools in the future will be those who understand ideas and how they fit with examples and applications.

MATH 52: Integral Calculus of Several Variables (5) math

Iterated integrals, line and surface integrals, vector analysis with applications to vector potentials and conservative vector fields, physical interpretations. Divergence theorem and the theorems of Green, Gauss, and Stokes.

MATH 53: Differential Equations with Linear Algebra, Fourier Methods, and Modern Applications (5) math

Ordinary differential equations and initial value problems, linear systems of such equations with an emphasis on second-order constant-coefficient equations, stability analysis for non-linear systems (including phase portraits and the role of eigenvalues), and numerical methods. Partial differential equations and boundary-value problems, Fourier series and initial conditions, and Fourier transform for non-periodic phenomena. Throughout the development we harness insights from linear algebra, and software widgets are used to explore course topics on a computer (no coding background is needed). The free e-text provides motivation from applications across a wide array of fields (biology, chemistry, computer science, economics, engineering, and physics) described in a manner not requiring any area-specific expertise, and it has an appendix on Laplace transforms with many worked examples as a complement to the Fourier transform in the main text.

MATH 104: Applied Matrix Theory (4) math

Linear algebra for applications in science and engineering. The course introduces the key mathematical ideas in matrix theory, which are used in modern methods of data analysis, scientific computing, optimization, and nearly all quantitative fields of science and engineering. While the choice of topics is motivated by their use in various disciplines, the course will emphasize the theoretical and conceptual underpinnings of this subject. Topics include orthogonality, projections, spectral theory for symmetric matrices, the singular value decomposition, the QR decomposition, least-squares methods, and algorithms for solving systems of linear equations; applications include clustering, principal component analysis and dimensionality reduction, regression. MATH 113 offers a more theoretical treatment of linear algebra. MATH 104 and ENGR 108 cover complementary topics in applied linear algebra. The focus of MATH 104 is on algorithms and concepts; the focus of ENGR 108 is on a few linear algebra concepts, and many applications.

MATH 107: Graph Theory (4) math

An introductory course in graph theory establishing fundamental concepts and results in variety of topics. Topics include: basic notions, connectivity, cycles, matchings, planar graphs, graph coloring, matrix-tree theorem, conditions for hamiltonicity, Kuratowski's theorem, Ramsey and Turan-type theorem.

MATH 108: Introduction to Combinatorics and Its Applications (4) math

Topics: graphs, trees (Cayley's Theorem, application to phylogony), eigenvalues, basic enumeration (permutations, Stirling and Bell numbers), recurrences, generating functions, basic asymptotics.

MATH 109: Groups and Symmetry (4) math

Applications of the theory of groups. Topics: elements of group theory, groups of symmetries, matrix groups, group actions, and applications to combinatorics and computing. Applications: rotational symmetry groups, the study of the Platonic solids, crystallographic groups and their applications in chemistry and physics. Honors math majors and students who intend to do graduate work in mathematics should take 120. WIM.

MATH 110: Number Theory for Cryptography (4) math

Number theory and its applications to modern cryptography. Topics include: congruences, primality testing and factorization, public key cryptography, and elliptic curves, emphasizing algorithms. Includes an introduction to proof-writing. This course develops math background useful in CS 255. WIM.

MATH 113: Linear Algebra and Matrix Theory (4) math

Algebraic properties of matrices and their interpretation in geometric terms. The relationship between the algebraic and geometric points of view and matters fundamental to the study and solution of linear equations. Topics: linear equations, vector spaces, linear dependence, bases and coordinate systems; linear transformations and matrices; similarity; dual space and dual basis; eigenvectors and eigenvalues; diagonalization. Includes an introduction to proof-writing. ( MATH 104 offers a more application-oriented treatment.)

ENGR 40M: An Intro to Making: What is EE (5) engr

Is a hands-on class where students learn to make stuff. Through the process of building, you are introduced to the basic areas of EE. Students build a 'useless box' and learn about circuits, feedback, and programming hardware, a light display for your desk and bike and learn about coding, transforms, and LEDs, a solar charger and an EKG machine and learn about power, noise, feedback, more circuits, and safety. And you get to keep the toys you build.

ENGR 76: Information Science and Engineering (5) engr

What is information? How can we measure and efficiently represent it? How can we reliably communicate and store it over media prone to noise and errors? How can we make sound decisions based on partial and noisy information? This course introduces the basic notions required to address these questions, as well as the principles and techniques underlying the design of modern information, communication, and decision-making systems with relations to and applications in machine-learning, through genomics, to neuroscience. Students will get a hands-on appreciation of the concepts via projects in small groups, where they will develop their own systems for streaming of multi-media data under human-centric performance criteria.

EE 108: Digital System Design (5) engr

Digital circuit, logic, and system design. Digital representation of information. CMOS logic circuits. Combinational logic design. Logic building blocks, idioms, and structured design. Sequential logic design and timing analysis. Clocks and synchronization. Finite state machines. Microcode control. Digital system design. Control and datapath partitioning. Lab

ENGR 108: Introduction to Matrix Methods (35) engr

Introduction to applied linear algebra with emphasis on applications. Vectors, norm, and angle; linear independence and orthonormal sets; applications to document analysis. Clustering and the k-means algorithm. Matrices, left and right inverses, QR factorization. Least-squares and model fitting, regularization and cross-validation. Constrained and nonlinear least-squares. Applications include time-series prediction, tomography, optimal control, and portfolio optimization. Undergraduate students should enroll for 5 units, and graduate students should enroll for 3 units. Prerequisites: MATH 51 or CME 100, and basic knowledge of computing ( CS 106A is more than enough, and can be taken concurrently). ENGR 108 and MATH 104 cover complementary topics in applied linear algebra. The focus of ENGR 108 is on a few linear algebra concepts, and many applications; the focus of MATH 104 is on algorithms and concepts.

EE 180: Digital Systems Architecture (4) engr

The design of processor-based digital systems. Instruction sets, addressing modes, data types. Assembly language programming, low-level data structures, introduction to operating systems and compilers. Processor microarchitecture, microprogramming, pipelining. Memory systems and caches. Input/output, interrupts, buses and DMA. System design implementation alternatives, software/hardware tradeoffs. Labs involve the design of processor subsystems and processor-based embedded systems. Formerly EE 108B.

BIO 82: Genetics (4) sci

The focus of the course is on the basic mechanisms underlying the transmission of genetic information and on the use of genetic analysis to study biological and medical questions. Major topics will include: (1) the use of existing genetic variation in humans and other species to identify genes that play an important role in determining traits and disease-susceptibility, (2) the analysis of mutations in model organisms and their use in the investigation of biological processes and questions and (3) using genetic information for diagnosis and the potential for genetic manipulations to treat disease.

BIO 83: Biochemistry & Molecular Biology (4) sci

Introduction to the molecular and biochemical basis of life. Lecture topics include the structure and function of proteins, nucleic acids, lipids and carbohydrates, energy metabolism, signal transduction, epigenetics and DNA repair. The course will also consider how defects in these processes cause disease.

BIO 85: Evolution (4) sci

Understanding evolution is key to understanding the diversity of life on earth. We will be focusing on the fundamental principles of evolutionary biology from natural and sexual selection to the formation of new species. To understand these concepts we will delve into the mechanisms that underlie them. The course will also link these fundamental processes to important contemporary evolutionary topics such as the evolution of behavior, life history evolution, and human evolution.

BIO 86: Cell Biology (4) sci

This course will focus on the basic structures inside cells and how they execute cellular functions. Topics include organelles, membrane trafficking, the cytoskeleton, cell division, and signal transduction.

PHYSICS 21: Mechanics and Fluids (3) sci

How are the motions of solids and liquids determined by the laws of physics? Students learn to describe the motion of objects (kinematics) and understand why objects move as they do (dynamics). Emphasis on applying Newton's laws to solids and liquids to describe diverse phenomena. Everyday examples are analyzed using tools of algebra and trigonometry. Problem-solving skills are developed, including verifying that derived results satisfy criteria for correctness, such as dimensional consistency and expected behavior in limiting cases. Physical understanding fostered by peer interaction and interactive group problem solving.

PHYSICS 23: Electricity, Magnetism, and Optics (4) sci

How are electric and magnetic fields generated by static and moving charges, and what are their applications? How is light related to electromagnetic waves? Students learn to represent and analyze electric and magnetic fields to understand electric circuits, motors, and generators. The wave nature of light is used to explain interference, diffraction, and polarization phenomena. Geometric optics is employed to understand how lenses and mirrors form images. These descriptions are combined to understand the workings and limitations of optical systems such as the eye, corrective vision, cameras, telescopes, and microscopes. Discussions based on the language of algebra and trigonometry. Physical understanding fostered by peer interaction and demonstrations in lecture, and interactive group problem solving in discussion sections.

PHYSICS 41: Mechanics (4) sci

Students learn to describe the motion of objects (kinematics) and then understand why motions have the form they do (dynamics). Emphasis on how the important physical principles in mechanics, such as conservation of momentum and energy for translational and rotational motion, follow from just three laws of nature: Newton's laws of motion. The distinction made between fundamental laws of nature and empirical rules that are useful approximations for more complex physics. Problems are drawn from examples of mechanics in everyday life. Skills developed in verifying that derived results satisfy criteria for correctness, such as dimensional consistency and expected behavior in limiting cases. Discussions based on the language of mathematics, particularly vector representations and operations, and calculus. Physical understanding is fostered by peer interaction and demonstrations in lecture, and discussion sections based on interactive group problem-solving.

PHYSICS 43: Electricity and Magnetism (4) sci

What is electricity? What is magnetism? How are they related? How do these phenomena manifest themselves in the physical world? The theory of electricity and magnetism, as codified by Maxwell's equations, underlies much of the observable universe. Students develop both conceptual and quantitative knowledge of this theory. Topics include: electrostatics; magnetostatics; simple AC and DC circuits involving capacitors, inductors, and resistors; integral form of Maxwell's equations; electromagnetic waves. Principles illustrated in the context of modern technologies. Broader scientific questions addressed include: How do physical theories evolve? What is the interplay between basic physical theories and associated technologies? Discussions based on the language of mathematics, particularly differential and integral calculus, and vectors. Physical understanding fostered by peer interaction and demonstrations in lecture, and discussion sections based on interactive group problem solving.

PHYSICS 61: Mechanics and Special Relativity (4) sci

This course covers Einstein's special theory of relativity and Newtonian mechanics at a level appropriate for students with a strong high school mathematics and physics background, who are contemplating a major in Physics or Engineering Physics or are interested in a rigorous treatment of physics. Postulates of special relativity, simultaneity, time dilation, length contraction, the Lorentz transformation, the space-time invariant, causality, relativistic momentum and energy, and invariant mass. Central forces, friction, contact forces, linear restoring forces. Momentum, work, energy, collisions. Angular momentum, torque, center of mass, moment of inertia, precession. Conserved quantities. Uses the language of vectors and multivariable calculus.

PHYSICS 81: Electricity and Magnetism Using Special Relativity and Vector Calculus (4) sci

This course recasts the foundations of electricity and magnetism in a way that will surprise, delight, and challenge students who have already encountered the subject at a college or AP level. Suitable for students contemplating a major in Physics or Engineering Physics, those interested in a rigorous treatment of physics as a foundation for other disciplines, or those curious about powerful concepts like transformations, symmetry, and conservation laws. Electrostatics and Gauss' law. Electric potential, electric field, conductors, image charges. Electric currents, DC circuits. Moving charges, magnetic field as a consequence of special relativity applied to electrostatics, Ampere's law. Solenoids, transformers, induction, AC circuits, resonance. Displacement current, Maxwell's equations. Electromagnetic waves. Throughout, we'll see the objects and theorems of vector calculus become manifest in charges, currents, and electromagnetic fields.

CME 100: Vector Calculus for Engineers (5)

Computation and visualization using MATLAB. Differential vector calculus: vector-valued functions, analytic geometry in space, functions of several variables, partial derivatives, gradient, linearization, unconstrained maxima and minima, Lagrange multipliers and applications to trajectory simulation, least squares, and numerical optimization. Introduction to linear algebra: matrix operations, systems of algebraic equations with applications to coordinate transformations and equilibrium problems. Integral vector calculus: multiple integrals in Cartesian, cylindrical, and spherical coordinates, line integrals, scalar potential, surface integrals, Green's, divergence, and Stokes' theorems. Numerous examples and applications drawn from classical mechanics, fluid dynamics and electromagnetism.

CME 102: Ordinary Differential Equations for Engineers (5)

Analytical and numerical methods for solving ordinary differential equations arising in engineering applications are presented. For analytical methods students learn to solve linear and non-linear first order ODEs; linear second order ODEs; and Laplace transforms. Numerical methods using MATLAB programming tool kit are also introduced to solve various types of ODEs including: first and second order ODEs, higher order ODEs, systems of ODEs, initial and boundary value problems, finite differences, and multi-step methods. This also includes accuracy and linear stability analyses of various numerical algorithms which are essential tools for the modern engineer. This class is foundational for professional careers in engineering and as a preparation for more advanced classes at the undergraduate and graduate levels.

CME 104: Linear Algebra and Partial Differential Equations for Engineers (5)

Linear algebra: systems of algebraic equations, Gaussian elimination, undetermined and overdetermined systems, coupled systems of ordinary differential equations, LU factorization, eigensystem analysis, normal modes. Linear independence, vector spaces, subspaces and basis. Numerical analysis applied to structural equilibrium problems, electrical networks, and dynamic systems. Fourier series with applications, partial differential equations arising in science and engineering, analytical solutions of partial differential equations. Applications in heat and mass transport, mechanical vibration and acoustic waves, transmission lines, and fluid mechanics. Numerical methods for solution of partial differential equations: iterative techniques, stability and convergence, time advancement, implicit methods, von Neumann stability analysis. Examples and applications drawn from a variety of engineering fields.

HUMBIO 2A: Genetics, Molecular Biology and Evolution (5)

Introduction to the principles of classical and modern genetics and evolutionary theory. Topics: micro- and macro-evolution, population and molecular genetics including personal genomics and CRISPR.

HUMBIO 3A: From Cells to Organisms (5)

Principles of the biology of cells and embryogenesis, emphasizing the development of humans and human tissues, the nature of membranes and organelles, signal transduction in healthy and diseased states (diabetes, cancer), stem cells and immunology.

HUMBIO 4A: The Human Organism (5)

Integrative Physiology: Neurobiology, endocrinology, and organ system function, control, and regulation.

PHIL 151: Metalogic (PHIL 251) (4) sci

In this course we will go through some of the seminal ideas, constructions, and results from modern logic, focusing especially on classical first-order ('predicate') logic. After introducing general ideas of induction and recursion, we will study a bit of elementary (axiomatic) set theory before then covering basic definability theory, viz. assessing the theoretical limits of what can and cannot be expressed in a first-order language. The centerpiece result of the class is the completeness - and closely related compactness - of first-order logic, a result with a number of momentous consequences, some useful, some philosophically puzzling. We will then study a connection with game theory, whereby a certain type of game characterizes precisely the expressive power of first-order logic. Further topics may include: the 0-1 law in finite model theory, second-order logic, and the algebraic approach to logic.