🐯 Princeton CS unofficial reading list (Fall 2025)

Spring 2026→

This is an unofficial list of books that are recommended in Fall 2025 computer science classes at Princeton
I’ve read sections of many of these, but the books marked with a bold ✔️ are ones I’ve been interested enough in to read at length

Freshman level
Computer Science: An Interdisciplinary Approach COS 126	• Computer Science: An Interdisciplinary Approach by Sedgewick & Wayne

Sophomore level
Intro. to Programming Systems COS 217	• C Programming: A Modern Approach by K.N. King • ARM 64-Bit Assembly Language by Pyeatt & Ughetta ✔️ *The Practice of Programming* by Kernighan & Pike ◦ Linux Pocket Guide by Daniel J. Barrett
Algorithms & Data Structures COS 226	✔️ *Algorithms* by Sedgewick & Wayne
Reasoning About Computation COS 240	• Mathematics for Computer Science by Lehman, Leighton & Meyer

Junior level
Mathematics for Numerical Computing & Machine Learning COS 302	✔️ *Mathematics for Machine Learning* by Deisenroth, Faisal & Ong
Contemporary Logic Design COS 306	• Digital Design with RTL Design, VHDL & Verilog by Frank Vahid
Computing & Optimization for the Physical & Social Sciences COS 323	◦ An Introduction to Optimization: With Applications to Machine Learning by Chong, Lu & Zak ◦ Algorithms by Dasgupta, Papadimitriou & Vazirani ◦ Introduction to Applied Linear Algebra: Vectors, Matrices & Least Squares by Boyd & Vandenberghe ◦ Linear Programming: Foundations and Extensions by Robert J. Vanderbei ◦ Convex Optimization by Boyd & Vandenberghe
Intro. to Machine Learning COS 324	• Introduction to Machine Learning by Arora, Park, Jacob & Chen • An Introduction to Statistical Learning by James, Witten, Hastie & Tibshirani • Speech & Language Processing by Jurafsky & Martin • Reinforcement Learning: An Introduction by Sutton & Barto ✔️ *Mathematics for Machine Learning* by Deisenroth, Faisal & Ong ◦ Deep Learning by Goodfellow, Bengio & Courville ◦ Introduction to Probability by Blitzstein & Hwang ◦ Learning Data Science: Data Wrangling, Exploration, Visualization & Modeling with Python by Lau, Gonzalez & Nolan ◦ Pattern Recognition & Machine Learning by Christopher M. Bishop ◦ Introduction to Machine Learning by Etienne Bernard
Functional Programming COS 326	✔️ *OCaml Programming: Correct + Efficient + Beautiful* by Michael Clarkson ◦ Real World OCaml: Functional Programming for the Masses by Madhavapeddy & Minsky
Great Ideas in Theoretical CS COS 330	◦ Introduction to Algorithms by Cormen, Leiserson, Rivest & Stein ◦ Algorithms by Jeff Erikson
Adv. Programming Techniques COS 333	✔️ *The Practice of Programming* by Kernighan & Pike ◦ Python in a Nutshell: A Desktop Quick Reference by Martelli, Ravenscroft, Hoden & McGuire ◦ Flask Web Development: Developing Web Applications with Python by Miguel Grinberg ◦ JavaScript: The Definitive Guide: Master the World’s Most-Used Programming Language by David Flanagan ◦ Beginning Software Engineering by Rod Stephens
Introduction to Robotics COS 346
Artificial Intelligence, Law & Public Policy COS 352	See website for paper list
Computer Architecture & Organization COS 375	✔️ *Computer Organization & Design: The Hardware Software Interface* by Patterson & Hennessy

Senior level
Distributed Systems COS 418	◦ The Go Programming Language by Donovan & Kernighan ◦ Distributed Systems: Principles & Paradigms by Tanenbaum & van Steen ◦ Guide to Reliable Distributed Systems: Building High-Assurance Applications & Cloud-Hosted Services by Kenneth P. Birman
Computer Graphics COS 426	• Computer Graphics with Open GL by Hearn, Baker & Carithers
Cryptography COS 433	◦ Introduction to Modern Cryptography by Katz & Lindell ◦ A Graduate Course in Applied Cryptography by Boneh & Shoup ◦ Foundations of Cryptography by Oded Goldreich
Computer Networks COS 461	• Computer Networking: A Top-Down Approach by Kurose & Ross
Design of Very Large-Scale Integrated (VLSI) Systems COS 462	• Digital Integrated Circuits: A Design Perspective by Rabaey, Chandrakasan & Nikolic

| Graduate level | | | :—: | — | | Fundamentals of Deep Learning
COS 514 | • Theory of Deep Learning by Sanjeev Arora | | Automated Reasoning about Software
COS 516 | See website for paper list
• The Calculus of Computation: Decision Procedures with Applications to Verification by Bradley & Manna | | Adv. Computer Systems
COS 518 | See website for paper list | | Adv. Algorithm Design
COS 521 | ◦ Algorithmic Game Theory by Nisan, Roughgarden, Tardos & Vazirani
◦ Randomized Algorithms by Motwani & Raghavan
◦ Online Computation & Competitive Analysis by Borodin & El-Yaniv
◦ The Probabilistic Method by Alon & Spencer
◦ Approximation Algorithms by Vijay V. Vazirani
◦ The Design of Approximation Algorithms by Williamson & Shmoys
◦ Spectral Graph Theory by Fan R.K. Chung | | Robot Planning Meets Machine Learning
COS 531 | ◦ Planning with Markov Decision Processes: An AI Perspective by Mausam & Kolobov
◦ Algorithms for Decision Making by Kochenderfer, Wheeler & Wray
◦ Planning Algorithms by Steven M. LaValle
◦ Artificial Intelligence: A Modern Approach by Norvig & Russell
◦ Probabilistic Machine Learning: An Introduction by Kevin P. Murphy
◦ Pattern Recognition & Machine Learning by Christopher M. Bishop
◦ Reinforcement Learning: An Introduction by Sutton & Barto
◦ Machine Learning Methods for Planning by Steven Minton | | Efficient Systems for Foundation Models
COS 597A | See website for paper list | | Neural Sensing, Modeling & Understanding
COS 597E | See website for paper list | | Probabilistic Topics in RL
COS 597R | See website for paper list
• Deep Learning by Goodfellow, Bengio & Courville
• Probabilistic Machine Learning: Advanced Topics by Kevin P. Murphy
• Reinforcement Learning: An Introduction by Sutton & Barto | | Domain-specific Computer Systems Architecture
COS 597V | • Computer Architecture: A Quantitative Approach by Hennessy & Patterson | <!– | Other technology courses | | | :—: | — | | Economics of the Internet & Artificial Intelligence: The Digital Revolution
ECO 326 | • AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t & How to Tell the Difference by Narayanan & Kapoor
• Networks, Crowds & Markets: Reasoning about a Highly Connected World by Easley & Kleinberg
◦ The Attention Deficit: Unintended Consequences of Digital Connectivity by Swati Bhatt
◦ How Digital Communication Technology Shapes Markets: Redefining Competition, Building Cooperation by Swati Bhatt |