🐯 Princeton CS unofficial reading list (Fall 2024)
Spring 2025 →
This is an unofficial list of books that are recommended in Fall 2024 computer science classes at Princeton
The books marked with a ✨ are ones I’ve been interested enough in to read at length
| Freshman level | |
|---|---|
| Computers in Our World COS / EGR 109 |
● Understanding the Digital World: What You Need to Know about Computers, the Internet, Privacy & Security by Brian Kernighan |
| Computer Science: An Interdisciplinary Approach COS 126 - 🌆 my notes |
● 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: Essential Commands by Daniel J. Barrett |
| Algorithms & Data Structures COS 226 - 🌆 my notes |
● 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 |
| 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 ● 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 ● ✨ Mathematics for Machine Learning ✨ by Deisenroth, Faisal & Ong ● Deep Learning by Goodfellow, Bengio & Courville ● Introduction to Probability by Blitzstein & Hwang ● Reinforcement Learning: An Introduction by Sutton & Barto ● Programming with Python by Software Carpentry ● Learning Data Science: Data Wrangling, Exploration, Visualization & Modeling with Python by Lau, Gonzalez & Nolan |
| Functional Programming COS 326 |
● ✨ OCaml Programming: Correct + Efficient + Beautiful ✨ by Michael Clarkson ● Real World OCaml: Functional Programming for the Masses by Madhavapeddy & Minsky |
| Advanced 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 |
| Senior level | |
|---|---|
| Computer Graphics COS 426 |
● Computer Graphics with Open GL by Hearn, Baker & Carithers |
| Cryptography COS 433 |
● Introduction to Modern Cryptography by Katz & Lindell ● Foundations of Cryptography by Oded Goldreich ● Tutorials on the Foundations of Cryptography: Dedicated to Oded Goldreich by Yehuda Lindell |
| Principles of Blockchains COS 470 |
● ✨ Bitcoin & Cryptocurrency Technologies: A Comprehensive Introduction ✨ by Narayanan, Bonneau, Felten, Miller & Goldfeder |
| Graduate level | |
|---|---|
| Theoretical Machine Learning COS 511 |
● Introduction to Online Convex Optimization by Elad Hazan ● Introduction to Online Nonstochastic Control by Hazan & Singh ● Prediction, Learning & Games by Cesa-Bianchi & Lugosi ● Understanding Machine Learning: From Theory to Algorithms by Shalev-Shwartz & Ben-David ● Boosting: Foundations& Algorithms by Schapire & Freund ● Reinforcement Learning: Theory& Algorithms by Agarwal, Jiang, Kakade & Sun ● Optimization for Machine Learning by Elad Hazan |
| Optimization for Machine Learning COS 512 |
● Convex Optimization: Algorithms and Complexity by Sebastien Bubeck ● Optimization for Machine Learning by Elad Hazan ● Convex Optimization by Boyd & Vandenberghe |
| Automated Reasoning about Software COS 516 |
● The Calculus of Computation: Decision Procedures with Applications to Verification by Bradley & Manna |
| Advanced 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 |
| Recent Advances in Graph Algorithms COS 597B |
See website for paper list |
| Programming Languages for Distributed Systems COS 597E |
See website for paper list |
| AI, Society & Education COS 597H |
● Causal Inference: What If by Hernan & Robins |
| Systems for Serving Generative AI COS 597K |
See website for paper list |
| Deep Dive into Large Languages COS 597R |
See website for paper list |
| Recent Advances in Wireless Networks COS 597S |
See website for paper list |