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.

This is a required co-requisite laboratory for CS 111.

This is a required co-requisite laboratory for CS 111.

This is a required co-requisite laboratory for CS 111.

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.

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. 

To enroll in CS 230, students need an explicit authorization of concept mastery from faculty of one of the following courses CS 111, CS 111M, CS 111X, or CS 112; or have taken CS 200. Students who did not take CS 111 or equivalent at Wellesley complete a placement questionnaire. 

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. 

To enroll in CS 230, students need an explicit authorization of concept mastery from faculty of one of the following courses CS 111, CS 111M, CS 111X, or CS 112; or have taken CS 200. Students who did not take CS 111 or equivalent at Wellesley complete a placement questionnaire. 

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. 

To enroll in CS 230, students need an explicit authorization of concept mastery from faculty of one of the following courses CS 111, CS 111M, CS 111X, or CS 112; or have taken CS 200. Students who did not take CS 111 or equivalent at Wellesley complete a placement questionnaire. 

Once the course fills, students can add themselves to the waitlist by filling out this form.

This course introduces the design and analysis of fundamental algorithms. It focuses on the basic skills needed to design efficient, correct algorithms and mathematically prove these properties. General problem-solving techniques covered: divide-and-conquer, dynamic programming, greediness, and probabilistic algorithms. Topics include: sorting, searching, graph algorithms, optimization, network flows, asymptotic analysis, compression, and NP-completeness.

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. 

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.

This is a required co-requisite 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.

In recent years, Machine Learning (ML) has been used in novel applications—from generating new art and music to systems that accurately and reliably predict outcomes of medical interventions in real-time. Faster computing hardware, large datasets, and the probabilistic paradigm of ML, which frames advances like neural networks within statistical learning, have enabled these developments. In this course, we introduce the foundational concepts behind the probabilistic paradigm of predictive ML: statistical model specification and learning. We will focus on connecting theory with real-world applications. Students will get hands-on experience building models for specific tasks, most taken from healthcare contexts, using probabilistic programming languages. While expanding our methodological toolkit, we will simultaneously introduce critical perspectives to examine the ethics of ML within sociotechnical systems. This course lays the foundation for advanced study and research in ML. Topics include: directed graphical models, deep regression/classification, frequentist learning, and model evaluation. For more information, see the course website: https://mogu-lab.github.io/cs245/.

Enrollment in this course is by permission of the instructor. Students interested in taking this course should fill out this Google Form.

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. Please consult this webpage for more information about the two versions of CS 304.

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.

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.