Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. Still the cleanest treatment of probabilistic ML.
Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer. The statistician's textbook, free online.
Murphy, K. (2022). Probabilistic Machine Learning: An Introduction. MIT Press. A comprehensive modern survey, also free online.
Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. MIT Press. The standard deep-learning reference.
Mohri, M., Rostamizadeh, A., and Talwalkar, A. (2018). Foundations of Machine Learning (2nd ed.). MIT Press. Rigorous learning theory.
Shalev-Shwartz, S., and Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms. Cambridge. A more accessible learning-theory text.
Sutton, R. S., and Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. The reinforcement-learning bible.
Belkin, M., Hsu, D., Ma, S., and Mandal, S. (2019). "Reconciling modern machine learning practice and the bias–variance trade-off." PNAS. The double-descent paper.
Breiman, L. (2001). "Statistical modelling: The two cultures." Statistical Science. The classic essay on the divide between statistics and ML.
Further Learning
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