This is an unofficial reading list from 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

Sophomore level

Algorithms & Data Structures (COS 226, fa26)

Junior level

Principles of Computer System Design (COS 316, sp26)

Compiling Techniques (COS 320, sp26)

Computing & Optimization for the Physical & Social Sciences (COS 323, fa26)

  • An Introduction to Optimization: With Applications to Machine Learning by Chong, Lu & Zak
  • Convex Optimization by Boyd & Vandenberghe
  • Linear Programming: Foundations & Extensions by Robert J. Vanderbei
  • Algorithms by Dasgupta, Papadimitriou & Vazirani

Computer Architecture & Organization (COS 375, fa26)

  • ✔️ Computer Organization & Design: The Hardware Software Interface by Patterson & Hennessy
Senior level  
Operating Systems
COS 417 (sp26)		 • Operating Systems: Three Easy Pieces by Arpaci-Dusseau & Arpaci-Dusseau
Theory of Algorithms
COS 423 (sp26)		 ◦ Intro. to Algorithms by Cormen et al.
◦ Algorithm Design by Kleinberg & Tardos
Information Security
COS 432 (sp26)		 ◦ 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 (sp26)		 • 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 (sp26)		 • 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 (sp26)		 ✔️ 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
Design of VLSI Systems
COS 462 (fa26)		 • Digital Integrated Circuits: A Design Perspective by Rabaey, Chandrakasan & Nikolic
Computer Architecture
COS 475 (sp26)		 • 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 (sp26)		 ◦ 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 (sp26)		 ◦ 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
Graduate level  
Theoretical Machine Learning
COS 511 (sp26)		 • Intro. to Online Convex Optimization by Elad Hazan
• Intro. to Online Control by Hazan & Singh
◦ Computing Machinery & Intelligence by A.M. Turing
◦ 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 et al.
◦ An Elementary Intro. to Modern Convex Geometry by Keith Ball
◦ Lecture Notes: Optimization for Machine Learning by Elad Hazan
◦ Lectures on Modern Convex Optimization by Ben-Tal & Nemirovski
Fairness in Machine Learning
COS 534 (sp26)		 ◦ Fairness & Machine Learning: Limitations & Opportunities by Barocas, Hardt & Narayanan
◦ Causal Inference: What If by Hernan & Robins
◦ Causal Inference in Statistics: A Primer by Pearl, Glymour & Jewell
◦ Bridging Prediction & Intervention Problems in Social Systems by Liu et al.
◦ Racial Disparities in Automated Speech Recognition by Koenecke et al.
◦ Careless Whisper: Speech-to-Text Hallucination Harms by Koenecke et al.
◦ Delayed Impact of Fair Machine Learning by Liu et .al
◦ When Bias Begets Bias: A Source of Negative Feedback Loops in AI Systems by Lydia T. Liu
◦ Maintaining Fairness Across Distribution Shift: Do We Have Viable Solutions for Real-World Applications? by Jessica Schrouff
Systems & Machine Learning
COS 568 (sp26)		  
Formal methods with & for Machine Learning
COS 598B (sp26)		  
Sum-of-Squares Proofs & Efficient Algorithms
COS 598C (sp26)		  
Empirical Research Methods for CS
COS 598D (sp26)		 See website for paper list
Machine Learning for Structural Biology
COS 598L (sp26)		 See website for paper list