🐻 Cornell AI unofficial reading list (Spring 2025)
←Fall ‘24
See also the Cornell CS unofficial reading list (Spring 2025)
This is an unofficial list of books that are recommended in Spring 2025 courses for the artificial intelligence minor at Cornell
The books marked with a ✨ are ones I’ve been interested enough in to read at length
| Foundations of AI: Machine Learning | |
|---|---|
| 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 |
| Foundations Machine Learning ECE 3200 |
● Pattern Recognition & Machine Learning by Christopher M. Bishop ● ✨ Mathematics for Machine Learning ✨ by Deisenroth, Faisal & Ong |
| Learning with Big Messy Data ORIE 3741 |
● Learning from Data: A Short Course by Abu-Mostafa, Magdon-Ismail & Lin ● An Introduction to Statistical Learning by James, Witten, Hastie & Tibshirani ● Feature Engineering& Selection: A Practical Approach for Predictive Models by Kuhn & Johnson ● Understanding Machine Learning: From Theory to Algorithms by Shalev-Shwartz & Ben-David ● Mining of Massive Datasets by Leskovec, Rajaraman & Ullman ● Foundations of Data Science by Blum, Hopcroft & Kannan ● Foundations of Machine Learning by Mohri, Rostamizadeh & Talwalkar ● Artificial Intelligence: A Modern Approach by Russell & Norvig ● Pattern Recognition & Machine Learning by Christopher M. Bishop |
| 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 | |
|---|---|
| Choices & Consequences in Computing CS 1340 / INFO 1260 |
● ✨ 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 |