MIT posts 12 free AI books
MIT (curated thread) published a collection of 12 free AI/ML books covering foundations, deep learning, reinforcement learning, probabilistic methods, fairness, and system design—packaged as a single learning resource. (x.com)
The curated list names specific textbooks and monographs including Foundations of Machine Learning (Mohri, Rostamizadeh, Talwalkar), Deep Learning (Goodfellow, Bengio, Courville), Reinforcement Learning: An Introduction (Sutton & Barto), the two-volume Probabilistic Machine Learning series (Kevin P. Murphy), and Fairness and Machine Learning (Barocas, Hardt, Narayanan). (wegrad.in) Fairness and Machine Learning is published online under a Creative Commons BY‑NC‑ND 4.0 license per the book site, while Sutton & Barto’s Reinforcement Learning second edition is available as an MIT Press‑hosted open resource under a Creative Commons noncommercial license. (fairmlbook.org) Kevin Murphy’s Probabilistic Machine Learning appears online as a multi‑volume series with accompanying code repositories (pyprobml/pyprobml) and downloadable HTML editions; the project lists Book 1 (Introduction) and Book 2 (Advanced Topics) on its official GitHub Pages. (probml.github.io) The collection also includes system‑oriented texts such as Machine Learning Systems (MLSysBook), an open‑access two‑volume textbook with 15+ chapters, 50+ lab exercises, and a public GitHub repo that advertises a hardcopy MIT Press edition planned for 2026. (mlsysbook.org) Multiple outlets reported MIT aggregating these open textbooks and course resources into a single curated resource in February 2026, and aggregator write‑ups reproduce direct links to each title’s official host (deeplearningbook.org, probml.github.io, fairmlbook.org, mlsysbook.ai, mitpress pages). (wegrad.in)
