🐻 Cornell CS unofficial reading list (Spring 2025)
← Fall 2024
See also the Cornell AI unofficial reading list (Spring 2025)
This is an unofficial list of books that are recommended in Spring 2025 computer science classes at Cornell
The books marked with a ✨ are ones I’ve been interested enough in to read at length
| Freshman level | |
|---|---|
| Intro. to Computing: A Design and Development Perspective CS 1110 - 🌆my notes |
● ✨ Think Python: How to Think Like a Computer Scientist ✨ by Allen B. Downey |
| Choices & Consequences in Computing CS 1340 |
● ✨ Nothing to Hide: The False Tradeoff between Privacy & Security ✨ by Daniel J. Solove ● ✨ Dark Matters: On the Surveillance of Blackness ✨ by Simone Browne ● The Algorithmic Foundations of Differential Privacy by Dwork & Roth ● ✨ The Code Book: The Science of Secrecy from Ancient Egypt to Quantum Cryptography ✨ by Simon Singh ● ✨ Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed ✨ by James C. Scott ● Bit by Bit: Social Research in the Digital Age by Matthew Salganik ● Robot Ethics 2.0: From Autonomous Cars to Artificial Intelligence by Lin, Jenkins & Abney ● ✨ Algorithms of Oppression: How Search Engines Reinforce Racism ✨ by Safiya Umoja Noble See website for paper list |
| Sophomore level | |
|---|---|
| UNIX Tools & Scripting CS 2043 |
|
| Object-Oriented Programming & Data Structures CS 2110 |
● Data Structures & Abstractions with Java by Carrano & Henry ● ✨ Object-Oriented Design & Data Structures ✨ by Myers & Kozen ● Principled Programming: Introduction to Coding in Any Imperative Language by Tim Teitelbaum |
| Junior level | |
|---|---|
| Data Structures & Functional Programming CS 3110 |
● ✨ OCaml Programming: Correct + Efficient + Beautiful ✨ by Michael Clarkson |
| Intro. to Computer Game Architecture & Adv. Topics in Computer Game Architecture CS 3152 & 4152 |
● Game Design Workshop: A Playcentric Approach to Creating Innovative Games by Tracy Fullerton ● Rules of Play: Game Design Fundamentals by Tekinbas & Zimmerman ● The Game Design Reader: A Rules of Play Anthology by Tekinbas & Zimmerman ● The Art of Game Design: A Book of Lenses by Jesse Schell ● Challenges for Games Designers: Non-Digital Exercises for Video Game Designers by Brathwaite & Schreiber ● A Theory of Fun for Game Design by Raph Koster ● Introduction to Game Development by Steve Rabin ● Andrew Rollings and Ernest Adams on Game Design by Rollings & Adams |
| Computer System Organization & Programming CS 3410 |
● ✨ Computer Organization & Design RISC-V Edition: The Hardware Software Interface ✨ by Patterson & Hennessy ● Modern C by Jens Gustedt ● Operating Systems: Three Easy Pieces by Arpaci-Dusseau & Arpaci-Dusseau |
| Foundations of AI Reasoning & Decision-Making CS 3700 |
● Artificial Intelligence: A Modern Approach by Russell & Norvig |
| Intro. to Machine Learning CS 3780 |
● Probabilistic Machine Learning: An Introduction by Kevin Murphy ● The Elements of Statistical Learning: Data Mining, Inference& Prediction by Hastie, Tibshirani & Friedman ● An Introduction to Statistical Learning by James, Witten, Hastie & Tibshirani ● Patterns, Predictions & Actions: Foundations of Machine Learning by Hardt & Recht ● Fairness & Machine Learning: Limitations & Opportunities by Barocas, Hardt & Narayanan ● Introduction to Linear Algebra by Gilbert Strang ● Linear Algebra & Learning from Data by Gilbert Strang ● Machine Learning by Tom M. Mitchell |
| Senior level | |
|---|---|
| Numerical Analysis: Linear and Nonlinear Problems CS 4220 |
● A First Course in Numerical Methods by Ascher & Greif ● Numerical Linear Algebra by Trefethen & Bau ● Applied Numerical Linear Algebra by James W. Demmel ● Introduction to Linear Algebra by Gilbert Strang ● Insight through Computing: A MATLAB Introduction to Computational Science and Engineering by Van Loan & Fan |
| Operating Systems CS 4410 |
● Operating Systems: Three Easy Pieces by Arpaci-Dusseau & Arpaci-Dusseau ● Concurrent Programming with Harmony by Robbert van Renesse ● The Design & Implementation of the FreeBSD Operating System by McKusick, Neville-Neil & Watson ● Understanding the Linux Kernel: From I/O Ports to Process Management by Bovet & Cesati ● The Little Book of Semaphores by Allen B. Downey ● ✨ Computer Organization & Design RISC-V Edition: The Hardware Software Interface ✨ by Patterson & Hennessy |
| Intro. to Computer Networks CS 4450 |
● Computer Networks: A Systems Approach by Peterson & Davie |
| Intro. to Computer Vision CS 4670 |
● Computer Vision: Algorithms & Applications by Richard Szeliski |
| Natural Language Processing CS 4740 |
● Speech & Language Processing by Jurafsky & Martin |
| Robot Learning CS 4756 |
● Modern Adaptive Control & Reinforcement Learning by Bagnell, Boots & Choudhury ● Probabilistic Robotics by Thrun, Burgard & Fox ● Reinforcement Learning: An Introduction by Sutton & Barto ● Probability Theory: The Logic of Science by Jaynes & Bretthorst |
| Intro. to Deep Learning CS 4782 |
● Dive Into Deep Learning by Zhang, Lipton, Li & Smola ● Probabilistic Machine Learning: An Introduction by Kevin Murphy ● The Elements of Statistical Learning: Data Mining, Inference& Prediction by Hastie, Tibshirani & Friedman ● Introduction to Linear Algebra by Gilbert Strang |
| Intro. to Reinforcement Learning CS 4789 |
● Reinforcement Learning: Theory and Algorithms by Agarwal, Jiang, Kakade & Sun ● Reinforcement Learning: An Introduction by Sutton & Barto |
| Intro. to the Theory of Computing CS 4810 |
● Automata & Computability by Dexter C. Kozen ● Introduction to Automata Theory, Languages & Computation by Hopcroft, Motwani & Ullman ● Introduction to the Theory of Computation by Michael Sipser ● Elements of the Theory of Computation by Lewis & Papadimitriou ● Introduction to Formal Language Theory by Michael A. Harrison ● Computability: An Introduction to Recursive Function Theory by Nigel Cutland |
| Quantum Computing CS 4813 |
● Introduction to Classical & Quantum Computing by Thomas G. Wong ● Quantum Computation & Quantum Information by Nielsen & Chuang ● Quantum Computing: Lecture Notes by Ronald de Wolf |
| Intro. to Analysis of Algorithms CS 4820 |
● Algorithm Design by Kleinberg & Tardos ● Algorithms Illuminated by Tim Roughgarden ● Introduction to Algorithms by Cormen, Leiserson, Rivest & Stein ● The Design & Analysis of Algorithms by Dexter Kozen |
| Probability, Vectors & Matrices in Computing CS 4850 |
● Foundations of Data Science by Blum, Hopcroft & Kannan ● Probability and Computing: Randomized Algorithms & Probabilistic Analysis by Mitzenmacher & Upfal ● Randomized Algorithms by Motwani & Raghavan ● Algorithm Design by Kleinberg & Tardos |
| Masters level | |
|---|---|
| Software Engineering CS 5150 |
● Software Engineering at Google: Lessons Learned from Programming Over Time by Winters, Manshreck & Wright ● Better Embedded System Software by Philip Koopman ● The Mythical Man-Month: Essays on Software Engineering by Frederick P. Brooks, Jr. ● Software Engineering by Ian Sommerville ● The Pragmatic Programmer: Your Journey to Mastery by Thomas & Hunt |
| Data Science in the Wild CS 5304 |
● Mining of Massive Datasets by Leskovec, Rajaraman & Ullman ● Interactive Data Visualization for the Web: An Introduction to Designing with D3 by Scott Murray ● Python Data Science Handbook: Essential Tools for Working with Data by Jake VanderPlas ● Think Stats by Allen B. Downey |
| System Security CS 5430 |
|
| Blockchains, Cryptocurrencies & Smart Contracts CS 5433 |
● ✨ Bitcoin& Cryptocurrency Technologies: A Comprehensive Introduction ✨ by Narayanan, Bonneau, Felten, Miller & Goldfeder ● Foundations of Distributed Consensus and Blockchains by Elaine Shi |
| Security & Privacy Concepts in the Wild CS 5435 |
See website for paper list |
| Physically Based Animation for Computer Graphics CS 5643 |
See website for paper list |
| Intro. to Computer Vision CS 5670 |
● Computer Vision: Algorithms& Applications by Richard Szeliski ● Geometric Invariance in Computer Vision by Mundy & Zisserman |
| Natural Language Processing CS 5740 |
● LM-class by Artzi, Wu & Gul ● Neural Network Methods for Natural Language Processing by Yoav Golberg ● Speech & Language Processing by Jurafsky & Martin ● Introduction to Natural Language Processing by Jacob Eisenstein ● Foundations of Statistical Natural Language Processing by Manning & Schutze ● Neural Machine Translation by Philipp Koehn ● Deep Learning by Goodfellow, Bengio & Courville ● The Little Book of Deep Learning by Francois Fleuret ● A Course in Machine Learning by Hal Daume III ● Linguistic Fundamentals for Natural Language Processing: 100 Essentials from Morphology and Syntax by Emily M. Bender ● Linguistic Structure Prediction by Noah Smith |
| Machine Learning Hardware & Systems CS 5775 |
● Efficient Processing of Deep Neural Networks by Sze, Chen, Yang & Emer See website for paper list |
| Doctoral level | |
|---|---|
| Adv. Programming Languages CS 6110 |
● Types & Programming Languages by Benjamin C. Pierce ● Formal Semantics of Programming Languages by Glynn Winskel ● Foundations for Programming Languages by John C. Mitchell ● Semantics of Programming Languages: Structures & Techniques by Carl A. Gunter ● Basic Category Theory for Computer Scientists by Benjamin C. Pierce |
| Adv. Compilers CS 6120 |
See website for paper list |
| Runtime Verification CS 6156 |
|
| Numerical Methods for Data Science CS 6241 |
● Numerical Methods for Data Science by David W. Bindel ● Afternotes on Numerical Analysis by G.W. Stewart ● Scientific Computing: An Introductory Survey by Michael T. Health ● Scientific Computing with Case Studies by Dianne P. O’Leary ● Accuracy & Stability of Numerical Algorithms by Nicholas J. Higham ● Applied Numerical Linear Algebra by James W. Demmel ● Numerical Linear Algebra by Trefethen & Bau ● Matrix Algorithms by G.W. Stewart ● Matrix Methods in Data Mining & Pattern Recognition by Lars Elden ● Numerical Optimization by Nocedal & Wright ● Practical Optimization by Gill, Murray & Wright ● Convex Optimization by Boyd & Vandenberghe ● Numerical Methods for Least Squares Problems by Ake Bjorck ● The Elements of Statistical Learning: Data Mining, Inference& Prediction by Hastie, Tibshirani & Friedman ● Pattern Recognition& Machine Learning by Christopher M. Bishop ● All of Statistics: A Concise Course in Statistical Inference by Larry Wasserman |
| Data to Decisions: Principles of Efficient Data Science CS 6386 |
|
| Computation for Content Creation CS 6682 |
|
| Robot Manipulation CS 6751 |
● Probabilistic Robotics by Thrun, Burgard & Fox ● Planning Algorithms by Steven M. LaValle ● Mechanics of Robotic Manipulation by Matthew T. Mason ● Modeling and Control of Robot Manipulators by Sciavicco & Siciliano ● Modern Robotics: Mechanics, Planning, and Control by Lynch & Park ● Springer Handbook of Robotics by Siciliano & Khatib |
| Human-Robot Interaction: Algorithms & Experiments CS 6754 |
● Computational Human-Robot Interaction by Thomaz, Hoffman & Cakmak ● Artificial Intelligence: A Modern Approach by Russell & Norvig ● Reinforcement Learning: An Introduction by Sutton & Barto ● Probabilistic Robotics by Thrun, Burgard & Fox See website for paper list |
| Special Topics in Complexity Theory CS 6817 |
● Analysis of Boolean Functions by Ryan O’Donnell |