🐻 Cornell AI unofficial reading list (Fall 2024)
Spring 2025→
See also the Cornell CS unofficial reading list (Fall 2024)
This is an unofficial list of books that are recommended in Fall 2024 courses for the artificial intelligence minor at Cornell
The books marked with a ✨ are ones I’ve been interested enough in to read all the way through
| Foundations of AI: Machine Learning | |
|---|---|
| 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 |
| Data Mining & Machine Learning STSCI 3740 |
● An Introduction to Statistical Learning by James, Witten, Hastie & Tibshirani ● The Elements of Statistical Learning: Data Mining, Inference & Prediction by Hastie, Tibshirani & Friedman |
| Foundations of AI: Reasoning | |
|---|---|
| Foundations of AI Reasoning & Decision-Making CS 3700 |
● Artificial Intelligence: A Modern Approach by Russell & Norvig |
| Foundations of AI: Ethics, Governance & Policy | |
|---|---|
| Ethics of Computing & Artificial Intelligence Technologies ENGRG 3605 |
● ACM Code of Ethics and Professional Conduct ● Computers, Ethics, and Society by Ermann & Shauf ● The GNU Manifesto by Richard Stallman ● Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence by Kate Crawford |
| Electives | |
|---|---|
| Foundations of Robotics CS 4750 / ECE 4770 / MAE 4760 |
● 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 |
| Text Mining History & Literature INFO 3350 |
● Introduction to Computation & Programming Using Python: With Application to Computational Modeling & Understanding Data by John V. Guttag ● Introduction to Cultural Analytics & Python by Melanie Walsh ● Speech & Language Processing by Jurafsky & Martin |
| Minds & Machines COGST 2621 / PHIL 2621 |
● Mindware: An Introduction to the Philosophy of Cognitive Science by Andy Clark ● The Pattern On The Stone: The Simple Ideas That Make Computers Work by W. Daniel Hillis ● Consciousness: An Introduction by Blackmore & Troscianko ● Who Needs Emotions?: The Brain Meets the Robot by Fellous & Arbib ● Intelligence Unbound: The Future of Uploaded & Machine Minds by Blackford & Broderick ● The Conscious Mind: In Search of a Fundamental Theory by David J. Chalmers ● The Character of Consciousness by David J. Chalmers ● Matter & Consciousness by Paul M. Churchland ● The Feeling Of What Happens: Body & Emotion in the Making of Consciousness by Antonio Damasio ● Brainstorms: Philosophical Essays on Mind & Psychology by Daniel C. Dennett ● Brainchildren: Essays on Designing Minds by Daniel C. Dennett ● Philosophy of Mind: Classical & Contemporary Readings by David J. Chalmers ● Computing the Mind: How the Mind Really Works by Shimon Edelman ● The Chaos Machine: The Inside Story of How Social Media Rewired Our Minds & Our World by Max Fisher ● Psychosemantics: The Problem of Meaning in the Philosophy of Mind by Jerry A. Fodor ● Artificial Minds by Stan Franklin ● The Information: A History, a Theory, a Flood by James Gleick ● Linguistics: An Introduction to Linguistic Theory by Curtiss, et al. ● Ethics of Artificial Intelligence by S. Matthew Liao ● Vision: A Computational Investigation into the Human Representation & Processing of Visual Information by David Marr ● How the Mind Works by Steven Pinker ● Words & Rules: The Ingredients Of Language by Steven Pinker ● The Tell-Tale Brain: A Neuroscientist’s Quest for What Makes Us Human by V.S. Ramachandran ● You Look Like a Thing & I Love You: How Artificial Intelligence Works & Why It’s Making the World a Weirder Place by Janelle Shane ● The Mind’s I: Fantasies & Reflections On Self & Soul by Hofstadter & Dennett ● ✨ What Is ChatGPT Doing& Why Does It Work? ✨ by Stephen Wolfram |