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These are books that are recommended in Fall 2024 classes for the Artificial Intelligence minor at Cornell
The books marked with a ✨ are ones I’ve liked enough to read all the way through
See also Cornell Computer Science - Fall 2024 Unofficial Reading List
| Junior Level | |
|---|---|
| Foundations of AI Reasoning & Decision-Making CS 3700 |
● Artificial Intelligence: A Modern Approach by Russell & Norvig |
| Introduction 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 and Other Kernel-based Learning Methods by Cristianini & Shawe-Taylor ● Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond by Scholkopf & Smola ● Pattern Recognition and 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, and Prediction by Hastie, Tibshirani & Friedman ● Causal Inference for Statistics, Social, and 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 |
| 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 and 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 and Consciousness by Paul M. Churchland ● The Feeling Of What Happens: Body and Emotion in the Making of Consciousness by Antonio Damasio ● Brainstorms: Philosophical Essays on Mind and Psychology by Daniel C. Dennett ● Brainchildren: Essays on Designing Minds by Daniel C. Dennett ● Philosophy of Mind: Classical and 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 and 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 and Processing of Visual Information by David Marr ● How the Mind Works by Steven Pinker ● Words and 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 and I Love You: How Artificial Intelligence Works and Why It’s Making the World a Weirder Place by Janelle Shane ● The Mind’s I: Fantasies And Reflections On Self & Soul by Hofstadter & Dennett ● ✨ What Is ChatGPT Doing … and Why Does It Work? by Stephen Wolfram ✨ |
| 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 |
| Text Mining History & Literature INFO 3350 |
● Introduction to Computation and Programming Using Python: With Application to Computational Modeling and Understanding Data by John V. Guttag ● Introduction to Cultural Analytics & Python by Melanie Walsh ● Speech and Language Processing by Jurafsky & Martin |
| Human-Computer Interaction Design INFO 3450 / COMM 3450 |
● Sketching User Experiences: Getting the Design Right and the Right Design by Bill Buxton ● The Encyclopedia of Human-Computer Interaction by Interaction Design Foundation ● The Design of Everyday Things by Don Norman |
| Probability Models & Inference STSCI 3080 / BTRY 3080 / ILRST 3080 |
● Mathematical Statistics and Data Analysis by John A. Rice ● Probability and Statistics by DeGroot & Schervish ● Introduction To Probability by Bertsekas & Tsitsiklis |
| Probability Models & Inference for the Social Sciences STSCI 3110 / ECON 3110 / ILRST 3110 |
● Probability and Statistics for Engineering and the Sciences by Jay L. Devore |
| Data Mining & Machine Learning STSCI 3740 |
● An Introduction to Statistical Learning by James, Witten, Hastie & Tibshirani ● The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Hastie, Tibshirani & Friedman |
| Senior Level | |
|---|---|
| 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 and Control of Robot Manipulators by Sciavicco & Siciliano ● Modern Robotics: Mechanics, Planning, and Control by Lynch & Park |
| Robot Learning CS 4756 |
● Modern Adaptive Control and 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 |
| Robot Perception ECE 4240 / MAE 4810 |
● Information-Driven Planning and Control by Ferrari & Wettergren ● Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Approaches by White & Sofge ● Introduction to Bayesian Networks by Finn V. Jensen ● Learning in Graphical Models by Michael I. Jordan ● Markov Random Fields for Vision and Image Processing by Blake, Kohli & Rother ● Nonlinear Systems by Hassan K. Khalil ● Optimal Control and Estimation by Robert F. Stengel ● Optimal Control Theory: An Introduction by Donald E. Kirk ● Linear Programming: Foundations and Extensions by Robert J. Vanderbei ● Bayesian Networks and Decision Graphs by Finn V. Jensen ● Introduction to Artificial Intelligence by Charniak & McDermott ● The Handbook of Artificial Intelligence by Cohen & Feigenbaum ● A Course in Fuzzy Systems and Control by Li-Xin Wang ● Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference by Judea Pearl ● Neural Networks and Artificial Intelligence for Biomedical Engineering by Hudson & Cohen ● Neural Networks: Theoretical Foundations and Analysis by Clifford Lau ● Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence by John H. Holland ● Reinforcement Learning: An Introduction by Sutton & Barto ● Multiagent Systems by Gerhard Weiss ● An Introduction to MultiAgent Systems by Michael Woolridge |
| Linear Models with Matrices STSCI 4030 / BTRY 4030 |
● Applied Regression Analysis: A Research Tool by Rawlings, Pantula & Dickey |