CS 110: Computing in the Age of AI (1) intro

The rapid advancement of artificial intelligence (AI) is transforming the way we work, interact, and make decisions. AI is integrated into applications and devices that are woven into our daily lives. How does AI work? What impact will AI have on individuals, communities, and our global society? This course aims to provide students with the knowledge and skills to become informed digital citizens in the age of AI, ready to navigate the digital landscape. Students will gain fundamental technical understanding of how computers, the Web, and AI work, and will study three programming languages: HTML5, CSS, and JavaScript. Students will also examine and discuss societal and ethical issues related to the Web and AI technologies, and consider responsible and future use of these technologies.

CS 110L: Lab: Computing in the Age of AI (0) intro

CS 110L/MAS 110L is a required co-requisite lab for CS 110/MAS 110.

CS 111: Comp Program & Prob Solving (1) intro

An introduction to problem-solving through computer programming. Students learn how to read, modify, design, debug, and test algorithms that solve problems. Programming concepts include control structures, data structures, abstraction, recursion, and modularity. Students explore these concepts in the context of interactive programs, data processing, and graphics or audio, using the Python programming language.

CS 111L: Lab: Computer Programming & Problem Solving (0) intro

Accompanying required laboratory for CS 111.

CS 111X: Computer Programming and Problem Solving (1) intro

An introduction to problem-solving through computer programming. Students learn how to read, modify, design, debug, and test algorithms that solve problems. Programming concepts include control structures, data structures, abstraction, recursion, and modularity. Students explore these concepts in the context of interactive programs, data processing, and graphics or audio, using the Python programming language. Students in this section will complete self-directed weekly labs. This section is intended for students who have some previous programming experience, but would benefit from more before taking CS 230. Students unsure about whether to take CS 111, CS 111X, or CS 230 as their first Wellesley CS course should complete the CS placement questionnaire.

CS 112: Intro Computing for the Sciences (1) intro

An introduction to problem-solving through computer programming with special focus on application to problems relevant to physics, chemistry, and biology. Students learn how to read, modify, design, debug, and test algorithms that solve problems. Programming concepts include control structures, problem solving strategies, abstraction, recursion, and modularity. Students explore these concepts in the context of interactive programs, data processing, and graphical and numerical analysis, using the Python programming language. Students are required to attend a 2.5-hour lab where they will apply concepts learned in lecture to study topics at the intersection of chemistry and physics. Topics might include: chemical kinetics, numerical integration, molecular dynamics, computational biology, Monte Carlo techniques, and basic statistical analysis.

CS 121: Intro to Game Design (1)

Video games are a popular form of interactive media that engage players in dynamic experiences through unprecedented combinations of storytelling, visualization, interactivity, and multi-sensory immersion. This course will introduce students to video game production and concepts. We will develop a framework for critically analyzing this medium, learn to identify effective strategies for creating games and describe what elements of design impact the final experience of a game. We’ll also identify the function of user agency in this medium to better understand how players are affected by representation in video games. Throughout the course, students will be asked to apply these concepts while building their own games and become familiar with the fundamentals of video game design.

CS 200: OO Programming Studio in Java (1) intro

CS200 introduces students to Java, and the Object Oriented Model of programming with hands-on instruction and experience, using active learning pedagogical approaches. Students will gain knowledge and reinforcement in fundamental programming and programming-related skills, including problem decomposition into smaller and more manageable sub-problems, designing in the Object Oriented Model, programming in Java, practicing fundamental constructs like conditionals, looping, usage of basic Data Structures, as well as debugging and testing techniques. In addition, attention will be paid in developing skills around project management, pair and team work, and identifying and evaluating reliable resources for the task at hand. With successful completion of this course, students are expected to be independent programmers and learners, and effective team members.

CS 204: Intro to Web Development (1) sys

This course introduces modern web development using HTML, CSS, and JavaScript. JavaScript is explored in detail, including scoping, closures, objects, classes, object-oriented programming, and modules. The jQuery library is also introduced, and the course covers event handling and Ajax interactions. Students will build web pages that manage data structures using menus and forms, and that save/restore that data from local storage resulting in a persistent, dynamic web application. Designed web pages will be modern, responsive, and accessible. The course also covers Bootstrap and the jQuery UI (User Interface) library.

CS 220: Human-Computer Interaction (1) humans

Human-Computer Interaction is one of the areas that have transformed the way we use computers in the last 30 years. Topics include methodology for designing and testing user interfaces, interaction styles (command line, menus, graphical user interfaces, virtual reality, tangible user interfaces), interaction techniques (including use of voice, gesture, eye movements), design guidelines, and user interface software tools. Students will design a user interface, program a prototype, and test the results for usability.

CS 221: Digital Worlds for Gaming (1) intro

Digital games visualize compelling worlds that can resemble real-life environments and imagine other-worldly spaces. These virtual realms frame our experience of games and their design dramatically impacts our interpretation of their narratives and mechanics. Designers code environments to shape player agency and weave complex relationships between game characters. This course will teach students to create digital worlds and critically assess them as politically rich spaces that convey meaning. Students will build both 2D and 3D digital environments, coding elements such as interactivity and non-player entities, crafting game experiences that tell meaningful stories. CS221 continues to explore the Unity Game Engine and topics introduced by CS121, but enrollment is suitable for any student with 100-level coding experience and an interest in game design.

CS 230: Data Structures (1) intro

An introduction to techniques and building blocks for organizing large programs. Topics include: modules, abstract data types, recursion, algorithmic efficiency, and the use and implementation of standard data structures and algorithms, such as lists, trees, graphs, stacks, queues, priority queues, tables, sorting, and searching. Students become familiar with these concepts through weekly programming assignments using the Java programming language.

CS 230P: Data Structures (1) intro

An introduction to techniques and building blocks for organizing large programs. Topics include: modules, abstract data types, recursion, algorithmic efficiency, and the use and implementation of standard data structures and algorithms, such as lists, trees, graphs, stacks, queues, priority queues, tables, sorting, and searching. Students become familiar with these concepts through *additional self-driven weekly programming practice using the Java programming language.

CS 230PL: Lab: Data Structures (0) intro

This is a required co-requisite laboratory for CS 230P.

CS 230X: Data Structures (1) intro

An introduction to techniques and building blocks for organizing large programs. Topics include: modules, abstract data types, recursion, algorithmic efficiency, and the use and implementation of standard data structures and algorithms, such as lists, trees, graphs, stacks, queues, priority queues, tables, sorting, and searching. Students become familiar with these concepts through weekly programming assignments using the Java programming language.

CS 231: Fundamental Algorithms (1) algs

An introduction to the design and analysis of fundamental algorithms. General techniques covered: divide-and-conquer algorithms, dynamic programming, greediness, probabilistic algorithms. Topics include: sorting, searching, graph algorithms, compression, cryptography, computational geometry, and NP-completeness.

CS 232: Artificial Intelligence (1) ai

What is artificial intelligence (AI) and should humans fear it as one of 'our biggest existential threats'? In this course, we will grapple with these difficult questions and investigate them in different ways. We will discuss the development of the field from the symbolic, knowledge-rich approaches of the 20th century AI (e.g., rule-based systems), to statistical approaches that rely on increasingly large amounts of data, including an overview of contemporary deep learning techniques. We will explore how to apply these techniques in several AI application areas, including robotics, computer vision, and natural language processing, and consider ethical issues around AI in society. By the end of the semester, students should be able to answer the starting questions in-depth and with nuance.

CS 234: Data, Analytics, and Visualization (1) ai

As the number of our digital traces continues to grow, so does the opportunity for discovering meaningful patterns in these traces. In this course, students will initially learn how to collect, clean, format, and store data from digital platforms. By adopting a computational approach to statistical analysis, students will then implement in code different statistical metrics and simulation scenarios for hypothesis testing and estimation. Finally, students will generate meaningful visualizations for data exploration and communicating results. Additionally, we will discuss the ethics of data collection and think critically about current practices of experimenting with online users. Students will work in groups to create their own datasets, ask an interesting question, perform statistical analyses and visualizations, and report the results.

CS 235: Theory of Computation (1) theory

This course offers an introduction to the theory of computation. Topics include languages, regular expressions, finite automata, grammars, pushdown automata, and Turing machines. The first part of the course covers the Chomsky hierarchy of languages and their associated computational models. The second part of the course focuses on decidability issues and unsolvable problems. The final part of the course investigates complexity theory.

CS 240: Foundations of Computer Systems (11.25) sys

This course examines how computers run programs, introducing key software and hardware abstractions and implementations between programming languages and transistors. The course traces representation and translation of data and programs through three broad topics in computer systems: computer hardware implementation, including digital logic, computer arithmetic, and machine organization; the hardware-software interface, including instruction set architecture, assembly code, and the C programming language; and abstractions for practical systems, including the physical memory hierarchy, the operating system process model, virtual memory, and memory management. Students complete extensive hands-on projects in hardware and software systems. Students are required to attend one three-hour laboratory weekly.

CS 242: Computer Networks (1) sys

A systems-oriented approach to data networks, including a theoretical discussion of common networking problems and an examination of modern networks and protocols. Topics include point-to-point links, packet switching, Internet protocols, end-to-end protocols, congestion control, and security. Projects may include client-server applications and network measurement tools.

CS 244: Machine Learning (1) ai

Machine learning is the science of teaching computers how to learn from observations. It is ubiquitous in our interactions with society, such as in face recognition, web search, targeted advertising, speech processing, and genetic analysis. It is currently at the forefront of research in artificial intelligence, and has been making rapid strides given the vast availability of data today. This course is a broad introduction to the field, covering the theoretical ideas behind widely used algorithms like decision trees, linear regression, support vector machines, and many more. We will also study practical applications of these algorithms to problems in a variety of domains, including vision, speech, language, medicine, and the social sciences.

CS 250: Research or Individual Study (1) special

CS 250H: Research or Individual Study (0.5) special

CS 251: Principles of Programming Languages (1) pls

This course introduces the principles underlying the design, semantics, and implementation of modern programming languages in major paradigms including function-oriented, imperative, and object-oriented.

CS 299: Research Methods for Ethics of Tech (1) impact

How do we educate the next generation of data scientists, software engineers, and user experience designers to think of their work as not just technical but also ethical?

CS 301: Compiler and Runtime Design (1) pls

This course covers principles and practice in the design and implementation of modern compilers and programming language runtime systems.

CS 304: Databases with Web Interfaces (1) sys

CS 304 is a course in full-stack web development.

CS 307: Computer Graphics (1) graphics

A survey of topics in computer graphics with an emphasis on fundamental techniques. Topics include: graphics hardware, fundamentals of three-dimensional graphics including modeling, projection, coordinate transformation, synthetic camera specification, color, lighting, shading, hidden surface removal, animation, and texture-mapping. We also cover the mathematical representation and programming specification of lines, planes, curves, and surfaces. Students will build graphics applications using a browser-based platform.

CS 313: Computational Biology (1) science

Many elegant computational problems arise naturally in the modern study of molecular biology. This course is an introduction to the design, implementation, and analysis of algorithms with applications in genomics. Topics include bioinformatic algorithms for dynamic programming, tree-building, clustering, hidden Markov models, expectation maximization, Gibbs sampling, and stochastic context-free grammars. Topics will be studied in the context of analyzing DNA sequences and other sources of biological data. Applications include sequence alignment, gene-finding, structure prediction, motif and pattern searches, and phylogenetic inference. Course projects will involve significant computer programming in Java. No biology background is expected.

CS 315: Data Science for the Web (1) sys

In the past decade, we have experienced the rise of socio-technical systems used by millions of people: Google, Facebook, Twitter, Wikipedia, etc. Such systems are on the one hand computational systems, using sophisticated infrastructure and algorithms to organize huge amounts of data and text, but on the other hand social systems, because they cannot succeed without human participation. How are such systems built? What algorithms underlie their foundations? How does human behavior influence their operation and vice-versa? In this class, we will delve into answering these questions by means of: a) reading current research papers on the inner-workings of such systems; b) implementing algorithms that accomplish tasks such as web crawling, web search, random walks, learning to rank, text classification, topic modeling; and c) critically thinking about the unexamined embrace of techno-solutionism using a humanistic lens.

CS 317: Mobile App Development (1) sys

Mobile devices have become more popular than desktops or laptops for communicating with others, accessing information, and performing computation. This course covers the principles and practice of developing applications for mobile devices, with an emphasis on features that distinguish them from desktop/laptop applications and web applications. Topics include: the functionality of modern smartphones and tablets, including device sensors, actuators, and communication; an iterative design process for apps that people find both useful and usable; designing and implementing mobile app interfaces and behaviors; and tools for developing software in teams. In this hands-on and programming-intensive course, groups will build web apps and mobile apps using a process that combines aspects of Human Computer Interaction and software engineering. This course begins by using the React JS framework to build interactive web apps out of modular components. It then transitions to React Native, a cross-platform component-based mobile app development environment for creating mobile apps that run on both iOS and Android devices. The course also explores how apps can leverage cloud databases to store and share information.

CS 320: Tangible User Interfaces (1) humans

Tangible user interfaces emerge as a novel human-computer interaction style that interlinks the physical and digital worlds. Extending beyond the limitations of the computer mouse, keyboard, and monitor, tangible user interfaces allow users to take advantage of their natural spatial skills while supporting collaborative work. Students will be introduced to conceptual frameworks, the latest research, and a variety of techniques for designing and building these interfaces. Developing tangible interfaces requires creativity as well as an interdisciplinary perspective. Hence, students will work in teams to design, prototype, and physically build tangible user interfaces.

CS 321: Extended Reality (1) humans

Mixed and Augmented Reality technologies combine virtual content with the physical environment, allowing people to interact with computers and digital content in exciting new ways. These emerging human-computer interaction paradigms have been applied to a variety of fields including medicine, education, design, entertainment, and play. This course introduces fundamental methods, principles, and tools for designing, programming, and testing mixed and augmented reality applications. Topics include the history of virtual and augmented reality, application domains, hardware for 3D input and display, tracking and registration, 3D perception, and societal implications. Students will work individually and in teams to develop novel virtual and augmented reality experiences.

CS 323: Social Computing (1) humans

Social Computing systems connect us to our closest friends, and globally to people all over the world. In recent decades, companies like Facebook, Snapchat, and even Amazon, have reshaped our social environments. All of these systems, at their core, are designed to facilitate interactions between people. What design decisions shape these systems? Students will learn the theoretical foundations of Social Computing drawn from the Social Sciences, and will learn software prototyping and design techniques to create new systems. This class will explore topics such as identity, anonymity, reputation, moderation, crowdsourcing, and social algorithms. Students will work in teams to design, prototype, and build social computing systems.

CS 325: Designing for Accessibility (1) humans

As technology increasingly integrates with our lives, how can we ensure that its design is inclusive of users' different abilities? CS 325 expands on the fundamentals of design and qualitative research to explore how technology can be made accessible for diverse users, with an emphasis on people with disabilities. In this course, we will read about and analyze approaches to inclusive technology, study how design intersects with disability justice, learn about the history of accessible and assistive technologies, understand how to create multimodal user experiences, learn accessible web programming, and test state-of-the-art tools. Students will also conduct a semester-long case study project in which they work in groups to identify accessibility issues on the Wellesley campus and work with the community to build appropriate technology solutions.

CS 331: Advanced Algorithms (1) algs

Explore advanced topics in the design and analysis of algorithms and data structures. The focus is on expanding your toolkit of problem-solving techniques and considering new settings that model real-world challenges. Topics may include: randomization, approximation algorithms, online and streaming settings, parallel and distributed computing, linear programming and LP rounding, optimization under uncertainty, bias and fairness in algorithms, and algorithmic foundations of data science and machine learning.

CS 333: Natural Language Processing (1) ai

Natural Language Processing (NLP) is the subfield of CS that focuses on language technology. Because language is one of the most complex human abilities, building computational technologies that involve language is both challenging and important. This course introduces NLP methods and applications. Students will (1) learn core NLP algorithms and models; (2) explore the challenges posed by different aspects of human language; (3) learn to evaluate ethical concerns about language technology; and (4) complete a series of projects to implement and improve NLP models. We will cover a range of techniques, including n-gram models, Bayesian classifiers, neural networks, and deep learning. Applications include parsing, sentiment analysis, machine translation, and language generation, as well as information retrieval tasks like summarization, topic modeling, and question-answering.

CS 334: Sem: Methods for Ethics of Tech (1) impact

How do we educate the next generation of data scientists and software engineers to think of their work as not just technical but also ethical? How do we get them to see that the social impact of their work requires that it be driven by sound ethical principles? The way that these questions are interrogated, discussed, and the sort of answers we might propose will be informed by a thoroughgoing interdisciplinary lens. Students will learn theoretical frameworks from both Philosophy and Computational and Data Sciences and work together to see how knowledge of frameworks from both disciplines serves to enrich our understanding of the ethical issues that face digital technologies, as well as empower us to find creative solutions. Central questions include: What kinds of ethical considerations are part of the everyday jobs of graduates working in digital technology, either in non-profit or for-profit organizations? What parts of the current liberal arts curriculum, if any, are preparing our graduates for the kinds of ethical decision-making they need to engage in? How to expand the reach of ethical reasoning within the liberal arts curriculum, in order to strengthen the ethical decision-making preparation? A key component in our collective efforts to engage with these questions will involve a sustained semester-long research project with Wellesley alums working in the field of digital tech.

CS 340: Modeling for Computer Systems (1) sys

This course focuses on modeling and specifying computer systems. Students will learn how to reason about the properties and expected behavior of modern software. Topics include designing specifications, property-based testing, model checking, and satisfiability solvers. We will use real-world case studies to motivate the analysis of reliable computer systems. By the end of the course, students will be able to (1) design specifications for the expected behavior of a system, (2) model system behavior using state-of-the-art tools with automated formal methods, and (3) identify and prevent software bugs. While prior experience with algorithm design and analysis is expected, the course will cover any necessary background in systems programming and formal methods.

CS 341: Operating Systems (1) sys

This course is designed to provide a solid foundation in the design and implementation of key concepts in existing operating systems. These concepts include process management, scheduling, multitasking, synchronization, deadlocks, memory management, file systems, and I/O operations. Throughout the course, the mechanism design aspects of these concepts will be discussed and assessed from the point of view of a programmer. Moreover, more modern operating systems will be explored, such as virtual operating systems.

CS 342: Computer Security (1) sys

An introduction to computer security and privacy. Topics will include privacy, threat modeling, software security, web tracking, web security, usable security, the design of secure and privacy preserving tools, authentication, anonymity, practical and theoretical aspects of cryptography, secure protocols, network security, social engineering, the relationship of the law to security and privacy, and the ethics of hacking. This course will emphasize hands-on experience with technical topics and the ability to communicate security and privacy topics to lay and expert audiences. Assignments will include technical exercises exploring security exploits and tools in a Linux environment; problem sets including exercises and proofs related to theoretical aspects of computer security; and opportunities to research, write, present, and lead discussions on security- and privacy-related topics. Students are required to attend an additional 70-minute discussion section each week.

CS 343: Distributed Computing (1) sys

What is the “cloud”? What is a distributed system? This course is for students interested in understanding the fundamental concepts and algorithms underlying existing distributed systems. By the end of this course, students will have the basic knowledge needed to work with and build distributed systems, such as peer-to-peer systems and cloud computing systems. Topics include MapReduce, Spark, communication models, synchronization, distributed file systems, coordination algorithms, consensus algorithms, fault-tolerance, and security.

CS 344: Deep Learning (1) ai

Deep learning is the study of how computers can learn from data in a manner inspired by neural connections in the human brain. It is revolutionizing how people and machines interact. This course explores the principles and practice of modern deep learning systems. Students will design and implement their own artificial neural networks as well as analyze massive deep learning models at the forefront of the field of machine learning. Deep learning algorithms such as convolutional neural networks and recurrent neural networks will be applied in a variety of domains, including medical diagnosis, self-driving cars, and large-language models. Students will further investigate the societal impacts and ethical considerations of these deep learning systems.

CS 349: Probabilistic Foundations of Machine Learning (1) ai

In recent years, Artificial Intelligence has enabled applications that were previously not thought possible—from systems that propose novel drugs or generate new art/music, to systems that accurately and reliably predict outcomes of medical interventions in real-time. But what has enabled these developments? Probabilistic Machine Learning, a paradigm that casts recent advances in Machine Learning, like neural networks, into a statistical learning framework. In this course, we introduce the foundational concepts behind this paradigm—statistical model specification, and statistical learning and inference—focusing on connecting theory with real-world applications and hands-on practice. This course lays the foundation for advanced study and research in Machine Learning. Topics include: directed graphical models, deep Bayesian regression/classification, generative models (latent variable models) for clustering, dimensionality reduction, and time-series forecasting. Students will get hands-on experience building models for specific tasks ,most taken from healthcare contexts, using a probabilistic programming language based in Python.

CS 350: Research or Individual Study (1) special

CS 350H: Research or Individual Study (0.5) special

CS 360: Senior Thesis Research (1) capstone

Students enroll in Senior Thesis Research (360) in the first semester and carry out independent work under the supervision of a faculty member. If sufficient progress is made, students may continue with Senior Thesis (370) in the second semester.

CS 365: Adv. Projects in Playable Media (1) humans

Students with a deep personal interest in digital game design and other forms of playable media will work in collaborative units to explore all aspects of the game development process while contributing to a semester-length project of their own devising. This course will require students to explore an ethical approach to game development that will introduce new practices for ideation, pitching, designing, playtesting, and versioning through an iterative process that will result in a finished game. This course is specifically designed for students who have moderate experience with game development through either curricular activities or by working on projects of their own. Students will be expected to have moderate levels of experience with the Unity Game Engine.

CS 366: Adv. Projects in Interactive Media (1) humans

Students with deep interest in interactive media will drive cutting-edge research that shapes and examines novel user experiences with technology. Students will work in small groups to identify a direction of research, explore and iterate over designs, prototype at varying fidelities, build working systems, consider ethical implications, conduct evaluative studies, and report findings. This course is designed for students who have experience in designing and implementing interactive media through either curricular activities or by working on projects. Students will be expected to have moderate levels of experience with front-end web development.

CS 370: Senior Thesis (1) capstone

Students enroll in Senior Thesis Research (360) in the first semester and carry out independent work under the supervision of a faculty member. If sufficient progress is made, students may continue with Senior Thesis (370) in the second semester.

MATH 225: Combinatorics and Graph Theory (1) math

Combinatorics is the art of counting possibilities: for instance, how many different ways are there to distribute 20 apples to 10 kids? Graph theory is the study of connected networks of objects. Both have important applications to many areas of mathematics and computer science. The course will be taught emphasizing creative problem-solving as well as methods of proof, such as proof by contradiction and induction. Topics include: selections and arrangements, generating functions, recurrence relations, graph coloring, Hamiltonian and Eulerian circuits, and trees.