Majors

Computer Science (BS)source 1

Courses

COSI 10a: Introduction to Problem Solving in Python (4) intro

Introduces computer programming and related computer science principles. Through programming, students will develop fundamental skills such as abstract reasoning and problem solving. Students will master programming techniques using the Python programming language and will develop good program design methodology resulting in correct, robust, and maintainable programs. Usually offered every semester.

COSI 12b: Advanced Programming Techniques in Java (4) intro

Studies advanced programming concepts and techniques utilizing the Java programming language. The course covers software engineering concepts, object-oriented design, design patterns and professional best practices. This is a required foundation course that will prepare you for more advanced courses, new programming languages, and frameworks. Usually offered every year.

COSI 21a: Data Structures and the Fundamentals of Computing (4) algs

Focuses on the design and analysis of algorithms and the use of data structures. Through the introduction of the most widely used data structures employed in solving commonly encountered problems. Students will learn different ways to organize data for easy access and efficient manipulation. The course also covers algorithms to solve classic problems, as well as algorithm design strategies; and computational complexity theory for studying the efficiency of the algorithms. Usually offered every year.

COSI 29a: Discrete Structures (4) math

Covers topics in discrete mathematics with applications within computer science. Some of the topics to be covered include graphs and matrices; principles of logic and induction; number theory; counting, summation, and recurrence relations; discrete probability. Usually offered every year.

COSI 45a: Effective Communication for Computer Scientists (2) communication

Teaches the basics of good oral communication and presentation, such as structuring a presentation, body language, eye contact, pace and appropriateness for the audience. It will cover, with practice, a range of speaking engagements majors might meet in academia and industry including: presentation of a research paper, software architecture proposal, business elevator pitch, research funding proposal, and so on. Students will present a project already created for a 100-level COSI elective. They will give the presentation in class, receive feedback based on the practices taught and then have a chance to give the presentation a second time. Usually offered every semester.

COSI 93a: Research Internship and Analysis (4) special

Provides students with an opportunity to work in a computer science research lab for one semester, pursuing a project that has the potential to produce new scientific results. Students and the faculty member mutually design a project for the semester that supports the research agenda of the group. Students must attend all research group meetings and present their findings in oral and written form at the end of the semester. The project typically includes background research, some lab work, and collaboration with other group members. Course requires signature of the instructor, is subject to the availability of undergraduate research positions, and is typically open only to juniors and seniors.

COSI 97a: Senior Field Project (4) capstone

Structured around a real-world Applied Computer Science problem, we work with an outside partner company, government agency or non-profit on a project that is important to them. 2-4 students form an agile team representing applications, networks, mobile, database, UX, design, and user requirements to create and deliver a solution. Each offering of the course is with a different partner. Usually offered every third year.

COSI 98a: Independent Study (4) special

Open to exceptional students who wish to study an area of computer science not covered in the standard curriculum. Usually offered every year.

COSI 98b: Independent Study (4) special

Open to exceptional students who wish to study an area of computer science not covered in the standard curriculum. Usually offered every year.

COSI 99d: Senior Research (4) special

Research assignments and preparation of a report under the direction of an instructor. Usually offered every year.

COSI 101a: Fundamentals of Artificial Intelligence (4) ai

Survey course in artificial intelligence. Introduction to Lisp and heuristic programming techniques. Topics include problem solving, planning natural language processing, knowledge representation, and computer vision. Usually offered every year.

COSI 102a: Software Entrepreneurship (4) entrepreneur

Covers the fundamental concepts needed to transform an idea for a software application into a viable IT business. The focus of the course is on software-based IT enterprises and the specific challenges and opportunities they present. Learn the 'Lean Startup' process in this course with a significant hands-on focus. Usually offered every year.

COSI 103a: Fundamentals of Software Engineering (4) softeng

In this course, you will learn (and practice) the design and construction of large bodies of software using modern software engineering practices, including object oriented design, test driven development, working data, and project management. You will be challenged to solve different kinds of problems, using different approaches and different tools. The course also aims to teach the basic and expected knowledge and practice within industry for entry level developers. Usually offered every year.

COSI 104a: Introduction to Machine Learning (4) ai

Machine learning is essential to gaining insights into large-scale data and making decisions in a wide spectrum of real world applications. This course will provide you a basic understanding of machine learning techniques (e.g., linear regression, logistic regression, decision trees, neural networks, clustering, state machines and Markov decision processes, etc.), how to evaluate their performance, and demonstrating how these models can be used to solve real-world problems. In addition, this course will give you hands-on experience utilizing these machine learning models. Usually offered every year.

COSI 105b: Software Engineering for Scalability (4) softeng

Covers some of the 'big ideas' that come in to play when building large, complex, and highly scaled software systems. We will look at both research and practice scaling architecture and software design. How do you design and architect large scale web based systems? What are the classic algorithms and patterns used to achieve massive scale? We will look at caching, database partitioning, queueing, messaging and more. And we apply this learning working in teams of students to design and implement their own version of the Twitter backend from the ground up, and then stress test and measure it's scalability using real world tools and technologies. Usually offered every year.

COSI 107a: Introduction to Computer Security (4) sys

Introduces students to foundational concepts in computer and network security. Focuses on topics essential in many areas of contemporary software development, including security for application software, uses of cryptography, secure communications in the World-Wide Web, and issues in data privacy. Across these areas students will learn from real-world systems analyzing systems for threats and vulnerabilities, mechanisms of attack, and methods to design and implement defenses against attack. Course assignments will include techniques for both attack and defense as well as written and oral presentations about security issues, design, and implementation. Usually offered every year.

COSI 112a: Modal, Temporal, and Spatial Logic for Language (4) theory

Examines the formal and computational properties of logical systems that are used in AI and linguistics. This includes (briefly) propositional logic and first order logic, and then an in-depth study of modal logic, temporal logic, spatial logic, and dynamic logic. Throughout the analyses of these systems, focuses on how they are used in the modeling of linguistic data. Usually offered every second year.

COSI 114a: Fundamentals of Natural Language Processing I (4) ai

Explores the computational properties of natural language and the foundations of the algorithms used to process it. Students will develop an understanding of basic statistical natural language processing (NLP) methods by implementing language analysis and classification algorithms in Python. Topics include corpus statistics, text classification, language modeling, and the computational techniques needed to support these tasks, with a focus on generative models (e.g., naive Bayes, hidden Markov models). Usually offered every year.

COSI 115b: Fundamentals of Natural Language Processing II (4) ai

Provides a fundamental understanding of the problems in natural language understanding by computers, and the theory and practice of current computational linguistic systems. Of interest to students of artificial intelligence, algorithms, and the computational processes of comprehension and understanding. Usually offered every year.

COSI 116a: Information Visualization (4) graphics

Introduces foundational principles, methods, and techniques of visualization to enable creation of effective visual representations of information. Covers the design and evaluation of novel visual encodings for diverse and heterogeneous data, including numerical, ordinal, nominal, and temporal data, network data, and multimedia data. Provides an overview of relevant principles of human vision, perception, and psychology related to the derivation of insights from visual analysis. Create visualizations in Tableau, Python, and JavaScript. Requires programming in Python, JavaScript, HTML, and CSS. Requires extensive writing including documentation, explanations, and discussions of findings from data analyses. Students will choose from datasets across diverse topics such as climate science, sustainability, urban planning, and healthcare data to develop their own visual analyses. Students will analyze data in groups and present their findings both in slide-form and in a writeup that will be publishable in an online setting. Usually offered every year.

COSI 119a: Autonomous Robotics (4) ai

Become part of the team developing 'Campus Rover', our long term project. Explore the fundamental 'big questions' in robotics: How do robots know what to do? How do they see the world? How do they know where they are? How do they know where to go? How do they control their bodies? How should robots behave around people? How can we get them to work together? Learn and understand Robot Operating System (ROS) and how software for robots is built. Solve gradually more advanced robotic problems, work with real robots in our Robotics Lab. This is a hands-on course, emphasizing real world implementations. Usually offered every year.

COSI 120a: Topics in Computer Systems (4) special

Content will vary from year to year. May be repeated for credit. Prerequisites may vary with the topic area; check with instructor for details. Usually offered every third year.

COSI 121b: Structure and Interpretation of Computer Programs (4) intro

An introduction to idioms of programming methodology, and to how programming languages work. Principles of functional programming, data structures and data abstraction; state, imperative and object-oriented programming; lazy data structures; how an interpreter works; metalinguistic abstraction and programming language design; syntax analysis, lexical addressing, continuations and explicit control; continuation-passing style, metacircular and register-machine compilers. Usually offered every year.

COSI 123a: Statistical Machine Learning (4) ai

Focuses on learning from data using statistical analysis tools and deals with the issues of designing algorithms and systems that automatically improve with experience. This course is designed to give students a thorough grounding in the methodologies, technologies, mathematics, and algorithms currently needed by research in learning with data. Usually offered every year.

COSI 125a: Human-Computer Interaction (4) humans

Covers the basic theory and concepts of human-computer interaction. Topics include methodology for designing and testing user interfaces, interaction styles and techniques, design guidelines, and adaptive systems. The laboratory work is designed to give the student practice in a set of basic techniques used in the area of human-computer interaction. Usually offered every second year.

COSI 126a: Unsupervised Learning and Data Mining (4) ai

Focuses on unsupervised learning and introduces the basic concepts of cluster analysis, feature selection, outlier detection for large-scale and big data analysis. Some advanced unsupervised topics, such as ranking, auto-encoder, generative adversarial network, and self-supervised learning will be introduced as well. Usually offered every second year.

COSI 127b: Database Management Systems (4) sys

Introduces database structure, organization, and languages. Studies relational and object-oriented models, query languages, optimization, normalization, file structures and indexes, concurrency control and recovery algorithms, and distributed databases. Usually offered every second year.

COSI 128a: Introduction to Computer Networking (4) sys

Provides a comprehensive introduction to the principles and practices of computer networking. Students will engage with the principles of data communication, network architectures, and protocols, including state-of-the-art networking technologies. Usually offered every year.

COSI 130a: Introduction to the Theory of Computation (4) theory

Introduces topics in the theory of computation, including: finite automata and regular languages, pushdown automata and context-free languages, context-sensitive languages and Type 0 languages, Turing machines and Church's thesis, the halting problem and undecidability, and introduction to NP and PSPACE complete problems. Usually offered every year.

COSI 131a: Operating Systems (4) sys

Fundamental structures of a computer system from hardware abstractions through machine and assembly language, to the overall structure of an operating system and key resource management abstractions. Usually offered every year.

COSI 132a: Information Retrieval (4) ai

Explores the theory and practice of textual information retrieval, including text indexing; Boolean, vector space and probabilistic retrieval models; evaluation; interfaces; linguistic issues; web search; QA and text classification. Students will implement algorithms and design and build a search-based application. Usually offered every year.

COSI 133a: Graph Mining (4) algs

Graphs and networks are a fundamental tool for modeling complex social, technological, and biological systems. This course covers the core methodologies and algorithms of graph and network mining techniques. Students learn methods and algorithms of graph and network mining, apply graph and network mining tools, and work on related homework and course projects. Usually offered every second year.

COSI 135b: Computational Semantics (4) ai

A study of the computational treatment of core semantic phenomena in language. After a review of first-order logic and the lambda calculus, the course focuses on three core topics: interrogative structures, including semantics of questions, question-answering systems, dialogue, entailment, commonsense knowledge; meaning update and revision; and computational lexical semantics. Usually offered every second year.

COSI 136a: Automatic Speech Recognition (4) ai

Explores speech recognizer core components and their underlying algorithms, surveying real applications. Covers phonetics, HMMs, finite state grammars, statistical language models, and industry standards for implementing applications, like VXML. Students build and analyze simple applications using a variety of toolkits. Usually offered every year.

COSI 138a: Computational Linguistics Second Year Seminar (4) communication

A seminar on research methods, writing, and presentations, and in abstract writing. Aims to help students learn to prepare and deliver oral presentations and written papers of their research work, according to the standards used and expected in this field, both in industry job and academic settings. This will be useful to students applying for jobs in industry or for further graduate work at the Ph.D. level, as well as for the work carried out in such jobs and academic study. Usually offered every year.

COSI 142a: Embedded Systems Development (4) sys

A project-based course that teaches the foundational aspects of embedded systems and the development process utilizing Raspberry Pi. Students will will create prototypes of embedded systems, including applications for IoT devices. Usually offered every year.

COSI 143b: Data Management for Data Science (4) sys

This experiential class will study techniques and systems for ingesting, processing, analyzing, and visualizing large data sets. The end goal of the class is to familiarize students with the data management tools and concepts that can support the full-stack of data science pipelines. Usually offered every second year.

COSI 146a: Principles of Computer System Design (4) sys

Topics on the design and engineering of computer systems: techniques for controlling complexity; strong modularity using client-server design; layering; naming; networks; security and privacy; fault-tolerant systems, atomicity and recovery; performance; impact of computer systems on society.

COSI 147a: Distributed Systems (4) sys

This course covers abstractions and implementation techniques for the design of distributed systems. Topics include: distributed state sharing, coherence, storage systems, naming systems, security, fault tolerance and replication, scalability and performance.

COSI 149b: Practical Machine Learning with Big Data (4) ai

In this experiential learning course, students will learn and practice machine learning techniques to tackle real problems in industry and/or interdisciplinary research.

COSI 150a: Compiler Design (4) pls

The goal of this course is to provide an overview of the theory and practice of Compiler Design. This course will cover the fundamental components of Compiler Design including scanning, lexical analysis, parsing, semantic analysis, static analysis, and code generation.

COSI 152a: Web Application Development (4) sys

Introduces web programming that covers the fundamental languages and tools, including HTML/CSS for page layout, javascript/ajax for client-side integration, and server-side programming in Java, Ruby, and SQL.

COSI 153a: Mobile Application Development (4) sys

Introduces the design and analysis of mobile applications that covers the architecture of mobile devices, APIs for graphical user interfaces on mobile devices, location-aware computing, social networking.

COSI 159a: Computer Vision (4) ai

Designed for undergraduate and graduate students majoring/minoring in computer science, the course covers core topics in image/video understanding, such as, object detection/recognition/tracking, image segmentation, image enhancement, visual relationship mining, 3D reconstruction, image generation, optical flow, and video segmentation.

COSI 164a: Introduction to 3-D Animation (4) graphics

Covers the fundamental concepts of 3-D animation and teaches both the theory underlying 3-D animation as well as the skills needed to create 3-D movies.

COSI 165b: Deep Learning (4) ai

Due to its powerful capability and excellent performance in solving real-world problems, deep learning has become one of the most important machine learning techniques. This course covers the core methods and algorithms of deep learning techniques.

COSI 166b: Capstone Project for Software Engineering (4) capstone

Teaches modern software engineering concepts, emphasizing rapid prototyping, unit testing, usability testing, and collaborative software development principles.

COSI 167a: Advanced Data Systems (4) sys

Explores the principles of designing data systems tackling challenges such as optimizing the use of ever-evolving hardware (storage, computation, network), ensuring efficient collection of incoming data, querying dynamic data collections, parallelizing query processing, and more. Students will gain a comprehensive understanding of the key drivers of innovation in data systems: hardware and workloads, delving into the detailed analysis of recent and anticipated trends in both areas. Usually offered every year.

COSI 175a: Multimedia Processing (4) ai

A seminar studying current research papers relating to: multi-media representation and compression, information extraction and understanding from images and video, object recognition and content based image and video retrieval, related applications of machine learning. Usually offered every second year.

COSI 177a: Scientific Data Processing in Matlab (4) science

Introduces scientific computing using Matlab. Programming concepts such as data types, vectors, conditional execution, loops, procedural abstraction, modules, APIs are presented. The course will present scientific techniques relevant to computational science, with an emphasis on image processing. Usually offered every second year.

COSI 180a: Algorithms (4) algs

Basic concepts in the design and analysis of algorithms. Usually offered every second year.

COSI 190a: Introduction to Programming Language Theory (4) pls

An introduction to the mathematical semantics of functional programming languages. Principles of denotational semantics; lambda calculus and its programming idiom; Church-Rosser theorem and Böhm's theorem; simply typed lambda calculus and its model theory: completeness for the full type frame, Statman's 1-section theorem and completeness of beta-eta reasoning; PCF and full abstraction with parallel operations; linear logic, proofnets, context semantics and geometry of interaction, game semantics, and full abstraction. Usually offered every second year.

MATH 8a: Introduction to Probability and Statistics (4) math

Discrete probability spaces, random variables, expectation, variance, approximation by the normal curve, sample mean and variance, and confidence intervals. Does not require calculus; only high school algebra and graphing of functions. Usually offered every semester.

MATH 10a: Techniques of Calculus (a) (4) math

Introduction to differential (and some integral) calculus of one variable, with emphasis on techniques and applications. Usually offered every semester in multiple sections.

MATH 15a: Linear Algebra (4) math

Matrices, determinants, linear equations, vector spaces, eigenvalues, quadratic forms, linear programming. Emphasis on techniques and applications. Usually offered every semester.

MATH 22a: Honors Linear Algebra (4) math

MATH 22a covers linear algebra. The material is similar to MATH 15a but with some additional content, a more theoretical emphasis, and more attention to proofs. Usually offered every fall.

MATH 36a: Probability (4)

Sample spaces and probability measures, elementary combinatorial examples. Conditional probability. Random variables, expectations, variance, distribution and density functions. Independence and correlation. Chebychev's inequality and the weak law of large numbers. Central limit theorem. Markov and Poisson processes.

MATH 122a: Numerical Methods and Big Data (4) math

Introduces fundamental techniques of numerical linear algebra widely used for data science and scientific computing. The purpose of this course is to introduce methods that are useful in applications and research. Usually offered every year.

MATH 124a: Optimization (4) math

Explores the theory of mathematical optimization and its fundamental algorithms, emphasizing problems arising in machine learning, economics, and operations research. Topics include linear and integer programming, convex analysis, and duality. Usually offered every second year.

MATH 125a: Mathematics for Machine Learning (4) math

Serves as a first course in machine learning and general data science, with a focus on the mathematics underlying the various modern machine learning algorithms. The course covers the fundamental concepts of statistical distribution, information theory, statistical learning, optimization and matrix factorizations, as well as classic algorithms such as tree methods, kernel methods and various neural network models. A few important real world examples of current interest will be considered such as computer vision, natural language processing, search engine, recommendation systems, finance, and biology. Usually offered every second year.

BIOL 51a: Biostatistics (4)

A basic introduction to methods of statistics and mathematical analysis applied to problems in the life sciences. Topics include statistical analysis of experimental data, mathematical description of chemical reactions, and mathematical models in neuroscience, population biology, and epidemiology.

PHYS 29a: Electronics Laboratory I (4) sci

Introductory laboratory in electronics. Topics to be covered are time constants, frequency response, rectification, amplification, radio reception, combinatorial logic, digital state machines, and analog-to-digital conversion. The class will solve first and second order differential equations directly and with the help of the complex exponential. Usually offered every spring.

ANTH 138a: Digital Cultures (4)

Examines the complex and often fraught relationships between digital technologies and human cultures. By thinking through digital technology’s relationships to structures like race, gender, sexuality, class, and ability, this course helps us explore the human components in the creation, circulation, and experience of digital technologies. What this class spotlights is that though digital technologies may seem materially new and technically innovative, they are built on longstanding power relations that structure both their construction and their circulation. Involves participatory research projects and group work. Usually offered every third year.

ECON 148b: Introduction to Machine Learning with Economic Applications (4)

Provides an understanding of machine learning techniques, considers how to evaluate their performance, and demonstrates how they can be used to solve real-world problems in economics and business. The course involves hands-on experience applying/implementing machine learning models using Python. Usually offered every year.

ECON 83a: Statistics for Economic Analysis (4)

A first course in statistical inference. Topics include descriptive statistics, probability, normal and binomial distributions, sampling distributions, point and interval estimation, properties of estimators, hypothesis testing, regression, and analysis of variance. Usually offered every semester.

LING 130a: Semantics I (4)

Explores the semantic structure of language in terms of the current linguistic theory of model-theoretic semantics. Topics include the nature of word meanings, categorization, compositionality, and plurals and mass terms. Usually offered every year.

PHIL 106b: Mathematical Logic (4) sci

We continue our rigorous investigation of logic that we began in Phil6A by studying the metatheory of formal systems. We begin with an introduction to sets, relations, and functions, after which we prove the Soundness, Completeness, and Löwenheim-Skolem Theorems for First-Order Logic. We end by examining Turing machines in order to introduce students to the notions of computability and undecidability, and to prepare them for the more advanced study of Gödel's Incompleteness Theorems. Usually offered every second year.

PHIL 115a: The Philosophy and Ethics of Technology (4) sci

From TikTok to Meta, and from CRISPR to ChatGPT, gamification, Extended Reality, and the struggle against climate change, dramatic advances in technology are shaping our world and our lives like never before. This course investigates the moral, social, and political implications of these and other new technologies. How should we understand privacy and surveillance in the age of metadata? Will emerging biotechnologies and life-tracking metrics allow us to re-engineer humanity? Should we edit our genes or those of our children to extend human lives and enhance human abilities? Can geoengineering resolve the climate crisis? How will AI and robotics change the work world? Can machines be “conscious” and what would it mean if they can? Will AI help us reduce bias and combat bigotry, or make things worse? What does the explosion of social media mean for human agency? How can we live an act in meaningful ways in a world increasingly dominated by technological and capital forces? This course will explore how technology and our attitudes towards it are transforming who we are, what we do, how we make friends, care for our health, and conduct our social and political lives. In doing so, we will also investigate fundamental philosophical and ethical questions about agency, integrity, virtue, “the good,” and what it means to be human in an uncertain and shifting world. Special one-time offering, spring 2024.

PSYC 51a: Statistics (4)

Covers the fundamentals of descriptive and inferential statistics. Techniques useful in the behavioral sciences will be emphasized. Students learn the theory of statistical decisions, practical application of statistical software, and how to analyze journal articles. Usually offered every semester.