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.

Units: 1

Max Enrollment: 40

Crosslisted Courses: MAS 110

Prerequisites: Fulfillment of the Quantitative Reasoning (QR) component of the Quantitative Reasoning & Data Literacy requirement. No prior background with computers is expected.

Instructor: Shaer

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Fall

Semesters Offered this Academic Year: Fall

Notes: Mandatory Credit/Non Credit.

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

Units: 0

Max Enrollment: 13

Crosslisted Courses: MAS 110L

Prerequisites: Fulfillment of the Quantitative Reasoning (QR) component of the Quantitative Reasoning & Data Literacy requirement. No prior background with computers is expected.

Instructor: Melnick

Typical Periods Offered: Fall

Semesters Offered this Academic Year: Fall

Notes:

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.

This course has a required co-requisite laboratory - CS 111L.

Units: 1

Max Enrollment: 30

Prerequisites: Fulfillment of the Quantitative Reasoning (QR) component of the Quantitative Reasoning & Data Literacy requirement. No prior background with computers is expected.

Instructor: Staff

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Spring; Fall

Semesters Offered this Academic Year: Fall; Spring

Notes: Does not fulfill the laboratory requirement. Mandatory Credit/Non Credit.

Accompanying required laboratory for CS 111.

Units: 0

Max Enrollment: 15

Prerequisites: None.

Instructor: Staff

Typical Periods Offered: Fall and Spring

Semesters Offered this Academic Year: Spring; Fall

Notes:

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.

Units: 1

Max Enrollment: 36

Prerequisites: Fulfillment of the Quantitative Reasoning (QR) component of the Quantitative Reasoning & Data Literacy requirement.

Instructor: Turbak

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Fall

Semesters Offered this Academic Year: Fall

Notes: Mandatory Credit/Non Credit.

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.

Units: 1

Max Enrollment: 24

Prerequisites: MATH 115 and fulfillment of the Quantitative Reasoning portion of the Quantitative Reasoning and Data Literacy requirement. Prerequisites or Co-requisites - one of the following; ASTR 107, CHEM 105, CHEM 105P, CHEM 116 / BISC 116, CHEM 120, BISC 110, BISC111, BISC 112, BISC 113, GEOS 101, GEOS 102, NEUR 100, PHYS 100, PHYS 104, PHYS 106, PHYS 107, PHYS 108.

Instructor: Sandu, Teich

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Spring

Semesters Offered this Academic Year: Spring

Notes:

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.

Units: 1

Max Enrollment: 18

Crosslisted Courses: MAS 121

Prerequisites: None. Open to First-Years and Sophomores. Juniors and Seniors by permission of the instructor.

Instructor: Tynes

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Fall and Spring

Semesters Offered this Academic Year: Fall; Spring

Notes:

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 200 is for students who earned credit in CS 111, and who did not receive a recommendation to continue with CS 230.

Units: 1

Max Enrollment: 18

Prerequisites: Fulfillment of the Quantitative Reasoning (QR) component of the Quantitative Reasoning & Data Literacy requirement. Prior background with computers is expected - CS111 or CS112, or permission of the instructor.

Instructor: Kakavouli, Delcourt

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Fall and Spring

Semesters Offered this Academic Year: Fall

Notes:

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.

Units: 1

Max Enrollment: 24

Prerequisites: CS 111 or permission of the instructor.

Instructor: Anderson

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Fall and Spring

Semesters Offered this Academic Year: Fall

Notes:

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.

Units: 1

Max Enrollment: 18

Prerequisites: One of the following courses - CS 111, CS 115/MAS 115.

Instructor: Staff

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Spring; Fall

Semesters Offered this Academic Year: Spring; Fall

Notes:

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.

Units: 1

Max Enrollment: 18

Crosslisted Courses: MAS 221

Prerequisites: Any 100-level CS course.

Instructor: Tynes

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Spring

Semesters Offered this Academic Year: Fall

Notes:

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. 

This course has a required co-requisite lab - CS 230L.

A student is required to have confirmation of authorization from the CS 111 faculty that is based on the mastery of the CS 111 concepts in order to enroll in CS 230. Students who did not take CS 111 at Wellesley and who wish to enroll in CS 230 should contact the CS department to take a placement questionnaire.

Units: 1

Max Enrollment: 24

Prerequisites: CS 111 or permission of the instructor

Instructor: Staff

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Spring; Fall

Semesters Offered this Academic Year: Fall; Spring

Notes: Does not fulfill the laboratory requirement.

Accompanying required laboratory for CS 230.

The grading option chosen for the lecture (CS 230) - either Letter Grade or Credit/Non Credit - will apply to the lab as well; the final grade is a single unified grade for both lecture and lab and is based on the grading option you choose for the lecture.

Units: 0

Max Enrollment: 15

Prerequisites: None.

Instructor: Staff

Typical Periods Offered: Fall and Spring

Semesters Offered this Academic Year: Spring

Notes:

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.

Students are required to attend an additional two-hour laboratory section each week. CS 230P is open to all students who did not meet the prerequisites for CS 230, or for students who took CS 111 before the last semester, or students who would appreciate additional academic support because of their prior MATH and CS experience. Class meetings are followed by a 30-minutes practice session to provide for extra time to work with the instructor after class.

This course has a required co-requisite laboratory - CS 230PL.

Units: 1

Max Enrollment: 36

Prerequisites: CS 111 or permission of the instructor.

Instructor: Metaxas

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Fall and Spring

Semesters Offered this Academic Year: Fall

Notes:

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

The grading option chosen for the lecture (CS 230P) - either Letter Grade or Credit/Non Credit - will apply to the lab as well; the final grade is a single unified grade for both lecture and lab and is based on the grading option you choose for the lecture.

Units: 0

Max Enrollment: 12

Prerequisites: CS 111 or permission of the instructor.

Instructor: Metaxas

Typical Periods Offered: Fall

Semesters Offered this Academic Year: Fall

Notes:

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.

CS230X is intended for students with significant prior experience in Java programming as demonstrated by a 5 in CS AP A, or equivalent demonstration of experience. Students in this section will complete self-directed weekly labs. If you did not take the CS AP A exam and would like to take this class, you may take the CS placement questionnaire to see if you qualify.

Units: 1

Max Enrollment: 24

Prerequisites: CS 111 or permission of the instructor.

Instructor: Grevet Delcourt

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Fall

Semesters Offered this Academic Year: Fall

Notes:

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.

Units: 1

Max Enrollment: 24

Prerequisites: (CS 230, CS 230P, or CS 230X) and MATH 225, or permission of the instructor.

Instructor: Staff

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Spring; Fall

Semesters Offered this Academic Year: Fall; Spring

Notes:

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. 

Units: 1

Max Enrollment: 24

Prerequisites: CS 111 and CS 230, or permission of the instructor.

Instructor: C. Anderson

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Every other year

Semesters Offered this Academic Year: Not Offered

Notes:

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.

Units: 1

Max Enrollment: 24

Prerequisites: CS 230 or permission of the instructor.

Instructor: Mustafaraj

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Degree Requirements: DL - Data Literacy (Formerly QRF); DL - Data Literacy (Formerly QRDL)

Typical Periods Offered: Fall

Semesters Offered this Academic Year: Not Offered

Notes:

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.

Units: 1

Max Enrollment: 24

Prerequisites: CS 230 (or CS 230P or CS 230X) and MATH 225, or permission of the instructor.

Instructor: Staff

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Spring; Fall

Semesters Offered this Academic Year: Fall; Spring

Notes:

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.

Units: 1.25

Max Enrollment: 24

Prerequisites: (CS 230 or CS 230P or CS 230X) and MATH 225, or permission of the instructor.

Instructor: Staff

Distribution Requirements: MM - Mathematical Modeling and Problem Solving; LAB - Natural and Physical Sciences Laboratory

Typical Periods Offered: Spring; Fall

Semesters Offered this Academic Year: Spring; Fall

Notes: This course satisfies the laboratory requirement.

Accompanying required laboratory for CS 240.

The grading option chosen for the lecture (CS 240) - either Letter Grade or Credit/Non Credit - will apply to the lab as well; the final grade is a single unified grade for both lecture and lab and is based on the grading option you choose for the lecture.

Units: 0

Max Enrollment: 15

Prerequisites: None.

Instructor: Staff

Typical Periods Offered: Fall and Spring

Semesters Offered this Academic Year: Spring

Notes:

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.

Units: 1

Max Enrollment: 18

Prerequisites: (CS230 or CS230P or CS230X) and Math 225 or permission of the instructor.

Instructor: Staff

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Every other year

Semesters Offered this Academic Year: Fall

Notes:

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.

Units: 1

Max Enrollment: 20

Prerequisites: (CS 230 or CS 230P or CS 230X) and MATH 225.

Instructor: Tjaden, Yacoby

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Spring

Semesters Offered this Academic Year: Fall

Notes: Mandatory Credit/Non Credit.

Units: 1

Max Enrollment: 25

Prerequisites: CS 230 or permission of the instructor.

Instructor:

Typical Periods Offered: Spring; Fall

Semesters Offered this Academic Year: Fall; Spring

Notes: Mandatory Credit/Non Credit

Units: 0.5

Max Enrollment: 15

Prerequisites: CS 230 or permission of the instructor.

Instructor:

Typical Periods Offered: Spring; Fall

Semesters Offered this Academic Year: Fall; Spring

Notes: Mandatory Credit/Non Credit

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. The course examines: language dimensions including syntax, naming, state, data, control, types, abstraction, modularity, and extensibility; issues in the runtime representation and implementation of programming languages; and the expression and management of parallelism and concurrency. Students explore course topics via programming exercises in several languages, including the development of programming language interpreters.

Units: 1

Max Enrollment: 24

Prerequisites: CS 230 or permission of the instructor.

Instructor: Turbak

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Spring; Fall

Semesters Offered this Academic Year: Spring

Notes:

Units: 1

Max Enrollment: 25

Crosslisted Courses: CS 299

Prerequisites: None.

Instructor: Walsh (Philosophy), Mustafaraj (Computer Science)

Distribution Requirements: REP - Religion, Ethics, and Moral Philosophy

Degree Requirements: DL - Data Literacy (Formerly QRF); DL - Data Literacy (Formerly QRDL)

Semesters Offered this Academic Year: Fall

Notes:

This course covers principles and practice in the design and implementation of modern compilers and programming language runtime systems. Topics include lexical analysis, parsing, symbols tables, semantic analysis, type checking, intermediate representations, program analysis and optimization, code generation, garbage collection, and other runtime support. As time permits, the course may also survey topics including just-in-time compilation, runtime optimization, concurrent runtime systems, or extended automatic program error detection. Students will construct a full compiler and runtime system for a simple statically-typed programming language over the course of the semester.

Units: 1

Max Enrollment: 18

Prerequisites: CS 230 and at least one of CS 240 or CS 251. CS 235 is recommended, but not required.

Instructor: Staff

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Every other year

Semesters Offered this Academic Year: Not Offered

Notes:

CS 304 is a course in full-stack web development. The stack comprises the front-end (typically a web browser), the back-end (a database for storing and retrieving user-contributed data) and the middleware that knits the two together. We will learn how to parse the incoming web request, route the request to the appropriate handler, retrieve data from the database that is relevant to the user's search, combine that data with static templates of web pages, and deliver that data to the browser. We will build endpoints to handle Ajax requests and learn about REST APIs.  We will also discuss performance, reliability,  concurrency, and security issues. In a semester project, we will create dynamic websites driven by database entries. In the fall, the CS 304 stack will comprise Flask and MySQL. In the spring, the CS 304 stack will comprise Node.js and MongoDB.

Units: 1

Max Enrollment: 24

Prerequisites: (CS 230 or CS 230P or CS 230X) and MATH 225 or permission of the instructor.

Instructor: Anderson

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Spring; Fall

Semesters Offered this Academic Year: Fall; Spring

Notes:

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.

Units: 1

Max Enrollment: 24

Prerequisites: CS 230 or permission of the instructor.

Instructor: Staff

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Spring; Fall

Semesters Offered this Academic Year: Spring

Notes:

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.

Units: 1

Max Enrollment: 18

Prerequisites: CS 230 or permission of the instructor.

Instructor: Tjaden

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Every other year

Semesters Offered this Academic Year: Not Offered

Notes:

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.

Units: 1

Max Enrollment: 18

Prerequisites: CS 230 or permission of the instructor.

Instructor: Mustafaraj

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Degree Requirements: DL - Data Literacy (Formerly QRDL)

Typical Periods Offered: Every other year

Semesters Offered this Academic Year: Not Offered

Notes:

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.

Units: 1

Max Enrollment: 18

Prerequisites: CS 230.

Instructor: Turbak

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Every other year

Semesters Offered this Academic Year: Not Offered

Notes:

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.

Units: 1

Max Enrollment: 18

Prerequisites: CS 220 or CS 230, or permission of the instructor.

Instructor: Shaer

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Spring

Semesters Offered this Academic Year: Spring

Notes:

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.

Units: 1

Max Enrollment: 18

Prerequisites: CS 220 or CS 221 or CS 230.

Instructor: Tynes

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Fall

Semesters Offered this Academic Year: Spring

Notes: Ann E. Maurer '51 Speaking Intensive Course.

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.

Units: 1

Max Enrollment: 18

Prerequisites: CS 220 or CS 230.

Instructor: Delcourt

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Every other year

Semesters Offered this Academic Year: Spring

Notes:

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.

Units: 1

Max Enrollment: 18

Prerequisites: CS 220 or permission of the instructor.

Instructor: Gadiraju

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Fall

Semesters Offered this Academic Year: Fall

Notes:

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.

Units: 1

Max Enrollment: 18

Prerequisites: CS 231 or permission of the instructor.

Instructor: Brubach

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Fall

Semesters Offered this Academic Year: Spring

Notes:

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.

Units: 1

Max Enrollment: 24

Prerequisites: CS 230 and either MATH 206 or MATH 220 or MATH 225.

Instructor: C. Anderson

Distribution Requirements: MM - Mathematical Modeling and Problem Solving; SBA - Social and Behavioral Analysis

Semesters Offered this Academic Year: Not Offered

Notes:

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.

Units: 1

Max Enrollment: 18

Crosslisted Courses: CS 334

Prerequisites: One course in Philosophy, Computer Science, MAS, or Statistics, and permission of the instructor.

Instructor: Walsh (Philosophy), Mustafaraj (Computer Science)

Distribution Requirements: REP - Religion, Ethics, and Moral Philosophy

Degree Requirements: DL - Data Literacy (Formerly QRF); DL - Data Literacy (Formerly QRDL)

Typical Periods Offered: Fall

Semesters Offered this Academic Year: Fall

Notes:

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.

Units: 1

Max Enrollment: 18

Prerequisites: CS 111 and CS 230, or CS 111 and permission of the instructor.

Instructor: VanHattum

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Every other year

Semesters Offered this Academic Year: Spring

Notes:

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.

Units: 1

Max Enrollment: 18

Prerequisites: CS 240 or permission of the instructor.

Instructor: Staff

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Every other year

Semesters Offered this Academic Year: Not Offered

Notes:

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.

Units: 1

Max Enrollment: 18

Prerequisites: CS 230 and CS 240 or permission of the instructor.

Instructor: Lerner

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Fall

Semesters Offered this Academic Year: Not Offered

Notes:

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.

Units: 1

Max Enrollment: 18

Prerequisites: CS 230 (required); CS 231 or CS 242 (recommended).

Instructor: Bassem

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Every other year

Semesters Offered this Academic Year: Spring

Notes:

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.

Units: 1

Max Enrollment: 18

Prerequisites: CS 230 and MATH 225.

Instructor: Tjaden

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Spring

Semesters Offered this Academic Year: Spring

Notes:

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.

Units: 1

Max Enrollment: 18

Prerequisites: (CS 230 or CS 230P or CS 230X) and MATH 225, and permission of the instructor.

Instructor: Yacoby

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Fall

Semesters Offered this Academic Year: Fall

Notes:

Units: 1

Max Enrollment: 15

Prerequisites: Permission of the instructor. Open to Juniors and Seniors.

Instructor:

Typical Periods Offered: Spring; Fall

Semesters Offered this Academic Year: Spring; Fall

Notes: Mandatory Credit/Non Credit

Units: 0.5

Max Enrollment: 15

Prerequisites: Permission of the instructor.

Instructor:

Typical Periods Offered: Spring; Fall

Semesters Offered this Academic Year: Fall; Spring

Notes: Mandatory Credit/Non Credit

Units: 1

Max Enrollment: 15

Prerequisites: Permission of the department.

Typical Periods Offered: Spring; Fall

Semesters Offered this Academic Year: Fall; Spring

Notes: 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.

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.

Units: 1

Max Enrollment: 18

Crosslisted Courses: MAS 365

Prerequisites: One of the following - CS 321, CS 221/MAS 221, CS 220, CS 320, or (CS 121/MAS 121 and CS 230), or permission of the instructor (portfolio must be able to demonstrate some previous experience with game development).

Instructor: Tynes

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Spring

Semesters Offered this Academic Year: Spring

Notes: This course may be used to fulfill the capstone requirement for the MAS major.

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.

Units: 1

Max Enrollment: 18

Crosslisted Courses: MAS 366

Prerequisites: One of the following - CS204, CS220, CS320 or CS323.

Instructor: Shaer

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Fall

Semesters Offered this Academic Year: Fall

Notes: This course may be used to fulfill the capstone requirement for the MAS major.

Units: 1

Max Enrollment: 25

Prerequisites: CS 360 and permission of the department.

Typical Periods Offered: Spring; Fall

Semesters Offered this Academic Year: Fall; Spring

Notes: 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.