🐻 Cornell CS unofficial reading list (Fall 2024)
Spring 2025→
See also the Cornell AI unofficial reading list (Fall 2024)
This is an unofficial list of books that are recommended in Fall 2024 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 & Development Perspective CS 1110, 🌆my notes |
|
| Intro. to Computing: An Engineering & Science Perspective CS 1112 |
● ✨ Think Python: How to Think Like a Computer Scientist ✨ by Allen B. Downey |
| Sophomore Level | ||
|---|---|---|
| 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 |
| Object-Oriented Design & Data Structures - Honors CS 2112 |
● Data Structures & Abstractions with Java by Carrano & Henry ● Data Structures & Problem Solving Using Java by Mark Allen Weiss ● Program Development in Java: Abstraction, Specification & Object-Oriented Design by Liskov & Guttag ● Java Precisely by Peter Sestoft ● Design Patterns: Elements of Reusable Object-Oriented Software by Gamma, Helm, Johnson & Vlissides ● Java in a Nutshell: A Desktop Quick Reference by Evans, Clark & Flanagan ● Effective Java: Best Practices for the Java Platform by Joshua Block ● ✨ Object-Oriented Design & Data Structures ✨ by Myers & Kozen |
|
| Mathematical Foundations of Computing CS 2800 |
● Discrete Mathematics & Its Application by Kenneth Rosen ● Essential Discrete Mathematics for Computer Science by Lewis & Zax ● Mathematics for Computer Science by Lehman, Leighton & Meyer |
| Junior Level | |
|---|---|
| Data Structure s& Functional Programming CS 3110 |
● ✨ OCaml Programming: Correct + Efficient + Beautiful ✨ by Michael Clarkson |
| Computer System Organization & Programming CS 3410 |
● Pro Git: Everything You Need to Know about Git by Chacon & Straub |
| Foundations of AI Reasoning & Decision-Making CS 3700 |
● Artificial Intelligence: A Modern Approach by Russell & Norvig |
| Intro. to Machine Learning CS 3780 |
● Understanding Machine Learning: From Theory to Algorithms by Shalev-Shwartz & Ben-David ● ✨ Mathematics for Machine Learning ✨ by Deisenroth, Faisal & Ong ● Machine Learning by Tom Mitchell ● Probabilistic Machine Learning by Kevin Murphy ● An Introduction to Support Vector Machines & Other Kernel-based Learning Methods by Cristianini & Shawe-Taylor ● Learning with Kernels: Support Vector Machines, Regularization, Optimization & Beyond by Scholkopf & Smola ● Pattern Recognition & Machine Learning by Christopher M. Bishop ● Introduction to Machine Learning by Ethem Alpaydin ● Pattern Classification by Duda, Hart & Stork ● The Elements of Statistical Learning: Data Mining, Inference& Prediction by Hastie, Tibshirani & Friedman ● Causal Inference for Statistics, Social & Biomedical Sciences: An Introduction by Imbens & Rubin ● Foundations of Statistical Natural Language Processing by Hamming & Schutze ● Introduction to Information Retrieval by Manning, Raghavan & Schutze ● Statistical Learning Theory by Vladimir N. Vapnik |
| Senior Level | |
|---|---|
| Numerical Analysis & Differential Equations CS 4120 |
● An Introduction to Numerical Analysis by Suli & Mayers |
| Systems Programming CS 4414 |
● Computer Systems: A Programmer’s Perspective by Bryant & O’Hallaron ● A Tour of C++ by Bjarne Stroustrup |
| Computer Architecture CS 4420 |
● Computer Architecture: A Quantitative Approach by Hennessy & Patterson ● Digital Design & Computer Architecture by Harris & Harris ● Superscalar Microprocessor Design by Mike Johnson ● Processor Architecture: From Dataflow to Superscalar & Beyond by Silc, Robic & Ungerer ● Modern Processor Design: Fundamentals of Superscalar Processors by Shen & Lipasti ● A Primer on Memory Consistency & Cache Coherence by Nagarajan, Sorin, Hill & Wood ● Principles & Practices of Interconnection Networks by Dally & Towles |
| Introduction to Computer Networks CS 4450 |
● Computer Networks: A Systems Approach by Peterson & Davie |
| Introduction to Computer Graphics CS 4620 |
● Fundamentals of Computer Graphics by Marschner & Shirley |
| Foundations of Robotics CS 4750 |
● Probabilistic Robotics by Thrun, Burgard & Fox ● Planning Algorithms by Steven M. LaValle ● Artificial Intelligence: A Modern Approach by Russell & Norvig ● Modelling & Control of Robot Manipulators by Sciavicco & Siciliano ● Modern Robotics: Mechanics, Planning & Control by Lynch & Park |
| 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 Computational Complexity CS 4814 |
● Computational Complexity: A Modern Approach by Arora & Barak ● Mathematics & Computation: A Theory Revolutionizing Technology & Science by Avi Wigderson ● Introduction to Theory of Computation by Michael Sipser |
| Intro. to Analysis of Algorithms CS 4820 |
● Algorithm Design by Kleinberg & Tardos ● Introduction to Algorithms by Cormen, Leiserson, Rivest & Stein ● Algorithms by Dasgupta, Papadimitriou & Vazirani ● The Design & Analysis of Computer Algorithms by Aho, Hopcroft & Ullman ● Computers & Intractability: A Guide to the Theory of NP-Completeness by Garey & Johnson ● The Design & Analysis of Algorithms by Dexter Kozen |
| Masters Level | |
|---|---|
| Applied High-Performance & Parallel Computing CS 5220 |
● Introduction to High Performance Computing for Scientists & Engineers by Hager & Wellein ● Structured Parallel Programming: Patterns for Efficient Computation by McCool, Robison & Reinders ● Programming Massively Parallel Processors: A Hands-on Approach by Kirk & Hwu ● Performance Optimization of Numerically Intensive Codes by Goedecker & Hoisie ● Principles of Parallel Programming by Lin & Snyder ● An Introduction to Parallel Programming by Peter Pacheco ● Parallel Programming: Techniques & Applications Using Networked Workstations & Parallel Computers by Wilkinson & Allen ● Pro Git: Everything You Need to Know about Git by Chacon & Straub ● The C Programming Language by Kernighan & Ritchie ● A Tour of C++ by Bjarne Stroustrup ● Python Essential Reference by David Beazley ● ✨ Think Python: How to Think Like a Computer Scientist ✨ by Allen B. Downey |
| Distributed Computing Principles CS 5414 |
● Distributed Systems by Sape Mullender See website for paper list |
| Adv. Computer Architecture CS 5420 |
● Parallel Computer Architecture: A Hardware/Software Approach by Culler & Singh |
| Virtual & Augmented Reality CS 5650 |
● Augmented Reality: Principles & Practice by Schmalstieg & Hollerer ● Virtual Reality by Steven M. LaValle |
| Optimization Methods CS 5727 |
● Introduction to Operations Research by Hillier & Lieberman ● Linear Programming: Foundations & Extensions by Robert J. Vanderbei ● Introduction to Mathematical Optimization by Matteo Fischetti |
| Applied Machine Learning CS 5785 |
● The Elements of Statistical Learning: Data Mining, Inference& Prediction by Hastie, Tibshirani & Friedman ● Probabilistic Machine Learning by Kevin Murphy ● Pattern Recognition & Machine Learning by Christopher M. Bishop |
| Deep Learning CS 5787 |
● Dive into Deep Learning by Zhang, Lipton, Li & Smola ● Deep Learning by Goodfellow, Bengio & Courville |
| Networks & Markets CS 5854 |
● A Course in Networks & Markets: Game-theoretic Models & Reasoning by Rafael Pass ● Networks, Crowds & Markets: Reasoning About a Highly Connected World by Easley & Kleinberg |
| Doctoral Level | |
|---|---|
| Software Engineering in the Era of Machine Learning CS 6158 |
See website for paper list |
| Matrix Computations CS 6210 |
● Matrix Computations by Golub & Van Loan ● Numerical Linear Algebra by Trefethen & Bau ● Applied Numerical Linear Algebra by James W. Demmel ● Accuracy & Stability of Numerical Algorithms by Nicholas J. Higham ● Matrix Algorithms by G.W. Stewart ● Large-Scale Numerical Optimization by Michael Saunders |
| Adv. Systems CS 6410 |
● The Design & Implementation of the 4.4BSD Operating System by McKusic, Bostic, Karels & Quarterman ● Understanding the Linux Kernel: From I/O Ports to Process Management by Bovet & Cesati ● The Little Book of Semaphores by Allen B. Downey ● Computer Networking: A Top-Down Approach by Kurose & Ross |
| Security & Privacy Technologies CS 6431 |
See website for paper list |
| Computational Imaging CS 6662 |
● Computer Vision: Algorithms& Applications by Richard Szeliski ● Computational Imaging by Bhandari, Kadambi & Raskar ● Foundations of Computer Vision by Torralba, Isola & Freeman |
| 3D Vision CS 6672 |
See website for paper list |
| Adv. Language Technologies CS 6740 |
See website for paper list |
| Deep Learning for Robotics CS 6758 |
● Modern Robotics: Mechanics, Planning & Control by Lynch & Park ● Reinforcement Learning: An Introduction by Sutton & Barto |
| Machine Learning Theory CS 6783 |
● Statistical Learning Theory & Sequential Prediction by Rakhlin & Sridharan ● Prediction, Learning & Games by Cesa-Bianchi & Lugosi ● Understanding Machine Learning: From Theory to Algorithms by Shalev-Shwartz & Ben-David ● Introduction to Online Convex Optimization by Elad Hazan ● A Gentle Introduction to Concentration Inequalities by Karthik Sridharan |
| Foundations of Reinforcement Learning CS 6789 |
● Reinforcement Learning: Theory & Algorithms by Agarwal, Jiang, Kakade & Sun |
| **Analysis of Algorithms CS 6820 |
● The Design & Analysis of Algorithms by Dexter Kozen ● Algorithm Design by Kleinberg & Tardos |
| Modern Prediction Paradigms: Responsible Machine Learning CS 6828 |
See website for paper list |
| Algorithmic Game Theory CS 6840 |
● Twenty Lectures on Algorithmic Game Theory by Tim Roughgarden ● Networks, Crowds& Markets: Reasoning About a Highly Connected World by Easley & Kleinberg ● Algorithmic Game Theory by Nisan, Roughgarden, Tardos & Vazirani |
| The Structure of Information Networks CS 6850 |
● Networks, Crowds & Markets: Reasoning About a Highly Connected World by Easley & Kleinberg ● Algorithmic Game Theory by Nisan, Roughgarden, Tardos & Vazirani |