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

• [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.
• [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, 2013.

All freely accessible (an MIT IP may be required)