Primary textbooks

• [M1] Probabilistic Machine Learning: An Introduction, Kevin Murphy, MIT Press, 2022.
• [M2] Probabilistic Machine Learning: Advanced Topics, Kevin Murphy, MIT Press, 2023.

Other recommended reading

• [Ba] Learning Theory from First Principles Bach, 2025.
• [B] Pattern Recognition and Machine Learning, Bishop; Springer, 2006.
• [B2] Deep Learning Foundations and Concepts, Bishop; Bishop, 2024.
• [HTF] The Elements of Statistical Learning, Hastie, Tibshirani, and Friedman; Springer, 2009.
• [HR] Patterns, Predictions, and Actions A story about machine learning Hardt and Recht; Princeton University Press, 2022.
• [SB]/[SSS] Understanding Machine Learning: From Theory to Algorithms, Shalev-Shwartz and Ben-David; Cambridge University Press, 2014.
• [JWHT] An Introduction to Statistical Learning, James, Witten, Hastie, and Tibshirani; Springer, 2023.
• [T] Deep learning theory lecture notes, Matus, Telgarsky; 2021.

All freely accessible (an MIT IP may be required)