CS 111
CS 111 - Comp Program & Prob Solving

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 are required to attend an additional two-hour laboratory section each week. Required for students who wish to major or minor in computer science or elect more advanced courses in the field.

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.

CS 115
CS 115/ MAS 115 - Computing for Socio-Techno Web

Technologies and services made available from Computer Science, such as online environments Facebook, Twitter, and Wikipedia, are integral in today's world. Many problems exist in our real world that transfer to and get amplified in the virtual world created by highly interconnected and ubiquitous computing. What are the basic technologies that enable all this innovation? How do these new environments affect our lives? This course aims to answer these questions through investigation of the socio-techno web. On the technical side we study three languages: HTML5, CSS, and basic JavaScript. We interweave the technical with the social aspects by examining issues introduced by the use of the Social Web. In the process we learn how computers work.

Units: 1

Max Enrollment: 30

Crosslisted Courses: MAS 115

Prerequisites: None. Open to First-Years and Sophomores. Juniors and Seniors by 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

Notes:

CS 121
CS 121/ MAS 121 - Intro to Game Design

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: 16

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:

CS 203
CS 203 - Computer Music

This course explores how computer code can be used to produce music by examining topics such as digital signal processing, synthesis, protocols, networking, and modeling. Students work with a computer environment called SuperCollider, an open-source software designed for real-time audio synthesis. Students complete weekly programming assignments and create music ranging from synthesizer tracks in popular songs to experimental algorithmic compositions.

Units: 1

Max Enrollment: 18

Prerequisites: CS 111 and MATH 115, or permission of the instructor.

Instructor:  Davis

Distribution Requirements: MM - Mathematical Modeling and Problem Solving; ARS - Visual Arts, Music, Theater, Film and Video

Typical Periods Offered: Every other year

Semesters Offered this Academic Year: Not Offered

Notes:

CS 204
CS 204 - Intro to Web Development

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:

CS 220
CS 220 - Human-Computer Interaction

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:

CS 221
CS 221/ MAS 221 - Digital Worlds for Gaming

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: Spring

Notes:

CS 230
CS 230 - Data Structures

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. Students are required to attend an additional two-hour laboratory section each week.

Units: 1

Max Enrollment: 24

Prerequisites: CS111 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.

CS 231
CS 231 - Fundamental Algorithms

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 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:

CS 232
CS 232 - Artificial Intelligence

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 230 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:

CS 234
CS 234 - Data, Analytics, and Visualization

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: Fall

Notes:

CS 235
CS 235 - Theory of Computation

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 and either 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:

CS 240
CS 240 - Foundations of Computer Systems

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 permission of the instructor.

Instructor: Staff

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

Typical Periods Offered: Spring; Fall

Semesters Offered this Academic Year: Fall; Spring

Notes: This course satisfies the laboratory requirement.

CS 242
CS 242 - Computer Networks

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: CS 230 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:

CS 250
CS 250 - Research or Individual Study

Units: 1

Max Enrollment: 25

Prerequisites: CS 230 or permission of the instructor.

Instructor:

Typical Periods Offered: Spring; Fall

Semesters Offered this Academic Year: Spring; Fall

Notes: Mandatory Credit/Non Credit

CS 250H
CS 250H - Research or Individual Study

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: Spring; Fall

Notes: Mandatory Credit/Non Credit

CS 251
CS 251 - Principles of Programming Languages

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: Staff

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Spring; Fall

Semesters Offered this Academic Year: Not Offered

Notes:

CS 301
CS 301 - Compiler and Runtime Design

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
CS 304 - Databases with Web Interfaces

A study of the three-layer architecture commonly used for Web-based applications such as e-commerce sites. We will learn to model and design databases using entity-relationship diagrams and the Standard Query Language (SQL) for managing databases. We will work with MySQL for the database tier. We will focus on Flask, a popular Python-based web micro-framework for the middleware. We will learn about implementing features using Ajax. We will review alternative technologies such as Node.js and MongoDB. We will also discuss performance, reliability,  concurrency, and security issues. Finally, we will create dynamic websites driven by database entries.

Units: 1

Max Enrollment: 24

Prerequisites: CS 230 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:

CS 305
CS 305 - Machine Learning

Machine learning is the science of teaching computers how to learn from observations. It is ubiquitous in our interactions with society, showing up in face recognition, web search, targeted advertising, speech processing, genetic analysis, and even Facebook's selection of posts to display. 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 support vector machines, neural networks, graphical models, decision trees, and many more. We will also study practical applications of these algorithms to problems in vision, speech, language, biology, and the social sciences.

Units: 1

Max Enrollment: 18

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

Instructor: Tjaden

Distribution Requirements: MM - Mathematical Modeling and Problem Solving; EC - Epistemology and Cognition

Typical Periods Offered: Fall and Spring

Semesters Offered this Academic Year: Spring; Fall

Notes: Mandatory Credit/Non Credit

CS 307
CS 307 - Computer 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.

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:

CS 313
CS 313 - Computational Biology

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:

CS 315
CS 315 - Data and Text Mining for the Web

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 amount 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

Typical Periods Offered: Every other year

Semesters Offered this Academic Year: Not Offered

Notes:

CS 317
CS 317 - Mobile App Development

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 mobile apps using a process that combines aspects of Human Computer Interaction and software engineering. This course will start by introducing MIT App Inventor as a mobile app prototyping tool, and will then use a cross-platform mobile app development environment for creating mobile apps that run on both iOS and Android devices.

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: Spring

Notes:

CS 320
CS 320 - Tangible User Interfaces

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: Every other year

Semesters Offered this Academic Year: Not Offered

Notes:

CS 321
CS 321 - Extended Reality

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: Fall

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

CS 323
CS 323 - Social Computing

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 CS230.

Instructor: Delcourt

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Every other year

Semesters Offered this Academic Year: Spring

Notes:

CS 331
CS 331 - Advanced Algorithms

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: Not Offered

Notes:

CS 333
CS 333 - Natural Language Processing

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: Fall

Notes:

CS 341
CS 341 - Operating Systems

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 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:

CS 342
CS 342 - Computer Security

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: Fall

Notes:

CS 343
CS 343 - Distributed Computing

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:

CS 349H
CS 349H - HCI in the Age of Automation

We are living in the age of automation, where artificial intelligence (AI) is integrated into apps and devices that are woven into our daily lives. Our dependency on automated systems such as expert systems, conversational agents, self-driving cars, and drones in daily tasks will increase in the near future, and will foster new forms of human-automation interaction.

How can we design human-automation interactions, which are intuitive, and promote safety, transparency, trust, productivity and wellbeing? How can we design automated systems that promote equity and dignity? What impact will automation have on individuals, communities, and our global society?

This course will begin by surveying human-interaction with various automated systems. We will then examine automation as a global issue with impact on privacy, the future of work and wellbeing, and the peril of propagating bias. Finally, we will consider the potential of automation to address global challenges in healthcare, food security, and sustainability.

Units: 0.5

Max Enrollment: 18

Prerequisites: CS111, and at least one 200 or 300 level course from another department which explores relevant issues (e.g. ANTH 232, AFR 213, ECON 229, ES 214, PHIL 222, SOC 320, CAMS 301, BIOC 324, POL1 303)

Instructor: Shaer

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Spring

Semesters Offered this Academic Year: Not Offered

Notes: Mandatory Credit/Non Credit

CS 350
CS 350 - Research or Individual Study

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: Fall; Spring

Notes: Mandatory Credit/Non Credit

CS 350H
CS 350H - Research or Individual Study

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

CS 360
CS 360 - Senior Thesis Research

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.

CS 370
CS 370 - Senior Thesis

Units: 1

Max Enrollment: 25

Prerequisites: CS 360 and permission of the department.

Typical Periods Offered: Spring; Fall

Semesters Offered this Academic Year: Spring; Fall

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.