Published on *Computer Science Department at Princeton University* (https://www.cs.princeton.edu)

If you are looking for current course information:

-View the Registrar's course offerings page [1] for courses offered this semester.

-Students and faculty can refer to the Undergraduate Announcement [2] for the academic year undergraduate course information.

Undergraduate courses are in the 100 - 400 listing. Graduate courses are in the 400 - 500 listing.

Computers are all around us. How does this affect the world we live in? This course is a broad introduction to computing technology for humanities and social science students. Topics will be drawn from current issues and events, and will include discussion of how computers work, what programming is and why it is hard, how the Internet and the Web work, security and privacy.

Cross-listed as EGR109.

Classes:
Two 90-minute lectures. Self-scheduled computer laboratory.

An introduction to computer science in the context of scientific, engineering, and commercial applications. The course will teach basic principles and practical issues, and will prepare students to use computers effectively for applications in computer science, physics, biology, chemistry, engineering, and other disciplines. Topics include: hardware and software systems; programming in Java; algorithms and data structures; fundamental principles of computation; and scientific computing, including simulation, optimization, and data analysis. No prior programming experience required. Video lectures, labs, precepts.

Cross-listed as EGR126.

Classes:
Video lectures, one or two classes and two precepts

Prerequisite(s):
No prior programming experience is required. Satisfies the SEAS computing requirement.

Introduction to programming systems, including modular programming, advanced program design, programming style, test, debugging and performance tuning; machine languages and assembly language; and use of system call services.

Classes:
Two 60-minute lectures and two 60-minute preceptorials.

Prerequisite(s):
COS 126, ISC 231-234, or approval by the COS placement officer.

This course surveys the most important algorithms and data structures in use on computers today. Particular emphasis is given to algorithms for sorting, searching, and string processing. Fundamental algorithms in a number of other areas are covered as well, including geometric algorithms, graph algorithms, and some numerical algorithms. The course will concentrate on developing implementations, understanding their performance characteristics, and estimating their potential effectiveness in applications.

Classes:
Two 90-minute lectures and one 90 minute precept

Prerequisite(s):
COS 126 or approval by the COS placement officer. Enrollment is limited to undergraduate students. Any graduate student interested in enrolling must apply by application only. Please contact ckenny@princeton.edu for the application..

See ISC231-236.

This course provides a comprehensive and practical background for students interested in continuous mathematics for computer science. The goal is to prepare students for higher-level subjects in artificial intelligence, machine learning, computer vision, natural language processing, graphics, and other topics that require numerical computation. The course focuses on tying together the underlying mathematical principles, numerical algorithms, and how they are used to solve problems computationally. Assignments consist of both conceptual problems and coding portions completed in Python.

Cross-listed as SML305 Statistics and Machine Learning [3].

Prerequisite(s):
COS 126 or equivalent familiarity with programming. Corequisite: MAT 202/204 or equivalent.

See ELE206 Department of Electrical and Computer Engineering [4].

See MUS314 Department of Music [5].

See MUS 315 Department of Music [5].

Prerequisite(s):
Students wishing to take the class should email the professor stating their musical and computer-science experience, describing their interest in the course.

A study of the design and analysis of operating systems. Topics include: processes, mutual exclusion, synchronization, semaphores, monitors, deadlock prevention and detection, memory management, virtual memory, processor scheduling, disk management, file systems, security, protection, distributed systems.

Classes:
Two 90-minute lectures.

Prerequisite(s):
217 and 226 or instructor's permission.

Understand the design and construction of compilers. Concepts include syntax analysis, semantics, code generation, optimization and run-time systems. Translation of imperative languages (such as C), functional languages (such as ML), and object-oriented languages (such as Java) will be studied. Students will implement a complete compiler for a small language.

Classes:
Three lectures.

Prerequisite(s):
COS 217 and COS 226 or instructor's permission.

See ORF363 Department of Operations Research & Financial Engineering [6].

Provides a broad introduction to different machine learning paradigms and algorithms, providing a foundation for further study or independent work in machine learning, artificial intelligence, and data science. Topics include linear models for classification and regression, support vector machines, neural networks, clustering, principal components analysis, Markov decision processed, planning, and reinforcement learning.

Classes:
Two 90-minute lectures and one 50 minute precept

Prerequisite(s):
MAT 201 or 203; MAT 202 or 204; COS 226; ORF 245, ORF 309 or MAT 385 or permission of instructor.

An introduction to the principles of typed functional programming. Programming recursive functions over structured data types and informal reasoning by induction about the correctness of those functions. Functional algorithms and data structures. Principles of modular programming, type abstraction, representation invariants and representation independence. Parallel functional programming, algorithms and applications.

Prerequisite(s):
COS 226 or with permission of the instructor.

This is a course about the practice of programming. Programming is more than just writing code. Programmers must also assess tradeoffs, choose among design alternatives, debug and test, improve performance, and maintain software written by themselves & others. At the same time, they must be concerned with compatibility, robustness, and reliability, while meeting specifications. Students will have the opportunity to develop these skills by working on their own code and in group projects.

Classes:
Two 90-minute lectures.

Prerequisite(s):
217 and 226 (as corequisite).

An introduction to mathematical topics relevant to computer science. Combinatorics, probability and graph theory will be covered in the context of computer science applications. The course will present a computer science approach to thinking and modeling. Students will be introduced to fundamental concepts such as NP-completeness and cryptography that arise from the world view of efficient computation.

Classes:
Two 90-minute lectures and one 50 minute precept

Prerequisite(s):
COS 126 and 226 (or sufficient mathematical background), and MAT 175, 202, 204. COS 226 can be taken along with COS 340 in the same term. Please contact the professor directly with any questions you may have..

See MAT375 Department of Mathematics [7].

This course introduces algorithms for analyzing DNA, RNA, and protein, the three fundamental molecules in the cell. Students will learn algorithms on strings, trees, and graphs and their applications in: sequence comparison and alignment; molecular evolution and comparative genomics; DNA sequencing and assembly; recognition of genes and regulatory elements; and RNA structure and protein interaction networks. Students will also implement algorithms and apply them to biological data.

Classes:
Two 90-minute lectures and one 50 minute precept

Prerequisite(s):
COS126, COS226. Some exposure to discrete math and basic probability (e.g. COS340).

See WWS351 Princeton School of Public and International Affairs [8].

Classes:
Two 50 minute lectures and one precept.

Prerequisite(s):
Students should have some background in either technology (computer science, engineering) or the social sciences (especially economics, politics, and sociology)..

**Links**

[1] https://registrar.princeton.edu/course-offerings/

[2] https://ua.princeton.edu/

[3] https://sml.princeton.edu/

[4] http://ece.princeton.edu/

[5] http://www.princeton.edu/music/

[6] http://orfe.princeton.edu/

[7] http://www.math.princeton.edu/

[8] http://wws.princeton.edu/

[9] https://www.cs.princeton.edu/courses/catalog?page=1

[10] https://www.cs.princeton.edu/courses/catalog?page=2

[11] https://www.cs.princeton.edu/courses/catalog?page=3