🐯 Princeton CS unofficial reading list (Spring 2026)
Fall 2026→
This is an unofficial reading list from Spring 2026 computer science classes at Princeton
I’ve read sections of many of these, but the items marked with a bold ✔️ are ones I’ve found interesting enough 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: Essential Commands by Daniel J. Barrett ✔️ UNIX Tutorial for Beginners by Michael Stonebank ✔️ A Guided Tour of Emacs ◦ Debugging with GDB: The GNU Source-Level Debugger ◦ The GNU Make Manual |
| Algorithms & Data Structures COS 226 |
✔️ Algorithms by Sedgewick & Wayne |
| Reasoning About Computation COS 240 |
| Junior level | |
|---|---|
| Principles of Computer System Design COS 316 |
◦ Principles of Computer System Design: An Intro. by Saltzer & Kaashoek ◦ BBR: Congestion-Based Congestion Control: Measuring Bottleneck Bandwidth & Round-Trip Propagation Time by Cardwell et al. |
| Compiling Techniques COS 320 |
◦ Real World OCaml: Functional Programming for the Masses by Madhavapeddy & Minsky ◦ The OCaml System ◦ The OCaml API ◦ Modern Compiler Implementation in ML by Andrew W. Appel |
| Intro. to Machine Learning COS 324 |
• Intro. to Machine Learning: Lecture Notes for COS 324 at Princeton University by Arora et al. • An Intro. to Statistical Learning by James et al. • Speech & Language Processing: An Intro. to Natural Language Processing, Computational Linguistics & Speech Recognition with Language Models by Jurafsky & Martin • Reinforcement Learning: An Intro. by Sutton & Barto ✔️ Mathematics for Machine Learning by Deisenroth, Faisal & Ong ◦ Deep Learning by Goodfellow, Bengio & Courville ◦ Intro. to Probability by Blitzstein & Hwang ◦ Python Numpy Tutorial (with Jupyter & Colab) by Justin Johnson ◦ Python Tutorial by W3Schools ◦ Programming with Python by Software Carpentry ◦ Learning Data Science: Data Wrangling, Exploration, Visualization & Modeling with Python by Lau, Gonzalez & Nolan ◦ Singular Value Decomposition Tutorial by Kirk Baker ◦ Primer on Relevant Mathematical Notation & Concepts by Ruth Fong ◦ Pattern Recognition & Machine Learning by Christopher M. Bishop ◦ Intro. to Machine Learning by Etienne Bernard See website for paper list |
| Adv. Programming Techniques COS 333 |
✔️ The Practice of Programming by Kernighan & Pike ◦ Python in a Nutshell: A Desktop Quick Reference by Martelli et al. ◦ 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 ◦ Pro Git: Everything You Need to Know about Git by Chacon & Straub ◦ The Python Tutorial ◦ The Python Language Reference ◦ The Python Standard Library ◦ Learning PHP, MySQL & JavaScript: A Step-by-Step Guide to Creating Dynamic Websites by Robin Nixon ◦ SQL Tutorial by W3Schools ◦ SQLite Tutorial ◦ HTML Tutorial by W3Schools ◦ The Road to React: Your Journey to Master Plain yet Pragmatic React.js by Robin Wieruch ◦ CSS Tutorial by W3Schools ◦ Bootstrap 5 Tutorial by W3Schools |
| Senior level | |
|---|---|
| Operating Systems COS 417 |
• Operating Systems: Three Easy Pieces by Arpaci-Dusseau & Arpaci-Dusseau |
| Theory of Algorithms COS 423 |
◦ Intro. to Algorithms by Cormen et al. ◦ Algorithm Design by Kleinberg & Tardos |
| Information Security COS 432 (ECE) |
◦ Security Engineering: A Guide to Building Dependable Distributed Systems by Ross Anderson ◦ Computer Security by Dieter Gollmann ◦ The Codebreakers: The Comprehensive History of Secret Communication from Ancient Times to the Internet by David Kahn ✔️ Practical Cryptography by Ferguson & Schneier ◦ Building Secure Software: How to Avoid Security Problems the Right Way by Viega & McGraw ◦ The Tangled Web: A Guide to Securing Modern Web Applications by Michal Zalewski |
| Intro. to Reinforcement Learning COS 435 (ECE) |
• Reinforcement Learning: From Foundations to Frontiers by Peter Henderson ◦ Reinforcement Learning: An Intro. by Sutton & Barto ◦ Reinforcement Learning, Bit by Bit by Lu et al. ◦ Bandit Algorithms by Lattimore & Szepesvari ◦ Algorithms for Reinforcement Learning by Csaba Szepesvari ◦ Mathematical Foundations of Reinforcement Learning by Shiyu Zhao ◦ An Introduction to Deep Reinforcement Learning by Francois-Lavet et al. ◦ Markov Decision Processes: Discrete Stochastic Dynamic Programming by Martin L. Puterman ◦ Theoretical Neuroscience: Computational And Mathematical Modeling of Neural Systems by Dayan & Abbott ◦ Spinning Up in Deep RL by OpenAI See website for paper list |
| Economics & Computation COS 445 |
• 445 Cheatsheet ◦ Game Theory, Alive by Karlin & Peres ◦ Networks, Crowds & Markets: Reasoning about a Highly Connected World by Easley & Kleinberg ◦ Algorithmic Game Theory by Nisam et al. ◦ Handbook of Computational Social Choice by Brandt et al. ✔️ Bitcoin & Cryptocurrency Technologies: A Comprehensive Intro. by Narayanan et al. |
| Innovating Across Tech, Business & Marketplaces COS 448 |
✔️ The Everything Store: Jeff Bezos & the Age of Amazon by Brad Stone (🌆 my highlights) • In the Plex: How Google Thinks, Works & Shapes Our Lives by Steven Levy ✔️ The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers by Ben Horowitz (🌆 my highlights) ◦ Venture Deals: Be Smarter Than Your Lawyer & Venture Capitalist by Feld & Mendelson ◦ The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail by Clayton M. Christensen ◦ Crush It!: Why Now Is The Time To Cash In On Your Passion by Gary Vaynerchuk ✔️ Art of the Start : The Time-Tested, Battle-Hardened Guide for Anyone Starting Anything by Guy Kawasaki ◦ Demand: Creating What People Love Before They Know They Want It by Slywotzky & Weber ◦ Rework by Fried & Heinemeier Hansson ✔️ Positioning: The Battle for Your Mind by Ries & Trout (🌆 my highlights) ◦ The Lean Startup: How Today’s Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses by Eric Ries ◦ Running Lean: Iterate From Plan A to a Plan That Works by Ash Maurya ◦ Lean Analytics: Use Data to Build a Better Startup Faster by Croll & Yoskovitz ◦ The Startup Owner’s Manual: The Step-by-Step Guide for Building a Great Company by Blank & Dorf ◦ Business Model Generation: A Handbook for Visionaries, Game Changers & Challengers by Osterwalder & Pigneur ◦ Delivering Happiness: A Path to Profits, Passion & Purpose by Tony Hsieh ◦ Powerful: Building a Culture of Freedom & Responsibility by Patty McCord ✔️ Crossing the Chasm: Marketing & Selling Disruptive Products to Mainstream Customers by Geoffrey A. Moore ✔️ Good to Great: Why Some Companies Make the Leap & Others Don’t by Jim Collins See website for article list |
| Computer Architecture COS 475 (ECE) |
• Computer Architecture: A Quantitative Approach by Hennessy, Patterson & Kozyrakis ◦ Modern Processor Design: Fundamentals of Superscalar Processors by Shen & Lipasti |
| Parallel Computing: Principles, Systems & Programming COS 476 (ECE) |
◦ Computer Architecture: A Quantitative Approach by Hennessy, Patterson & Kozyrakis ◦ Parallel Computer Architecture: A Hardware/Software Approach by Culler, Singh & Gupta ◦ Programming Massively Parallel Processors: A Hands-on Approach by Hwu, Kirk & Hajj |
| Natural Language Processing COS 484 |
◦ Speech & Language Processing: An Intro. to Natural Language Processing, Computational Linguistics & Speech Recognition with Language Models by Jurafsky & Martin ◦ Intro. to Natural Language Processing by Jacob Eisenstein ◦ Foundations of Statistical Natural Language Processing by Manning & Schutze See website for paper list |