🐯 Princeton CS unofficial reading list (Fall 2025)
←Spring 2025
This is an unofficial list of books that are recommended in Fall 2025 computer science classes at Princeton
The books marked with a ✨ are ones I’ve been interested enough in to read at length
| Freshman level | |
|---|---|
| CS: 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 |
|
| 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 |
| 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 |
| B.S.E. Basic Math and Science | |
|---|---|
| Calculus I / II MAT 103 / 104 |
◦ Thomas’ Calculus: Early Transcendentals, Single Variable by Hass, Heil, Bogacki & Weir |
| Multivariable Calculus MAT 201 |
◦ Thomas’ Calculus: Multivariable by Hass, Heil, Bogacki & Weir |
| Linear Algebra with Applications MAT 202 |
◦ Linear Algebra with Applications by Otto Bretscher |
| General Chemistry: Applications in Modern Technology CHM 207 |
• Chemical Principles by Zumdahl & DeCoste |