🐯 Princeton CS unofficial reading list (Spring 2026)
←Fall 2025
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 |
📔 Course notes 📕 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 |
📕 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. 📄 445 Cheatsheet |
| 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 📕 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 2.0: 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 📕 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 |