Machine Learning

Rigorous treatment of ML foundations covering PAC learning, Rademacher complexity, boosting, and kernel methods. Ideal for readers with strong mathematical background.

Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar

Comprehensive lecture notes on supervised ML techniques from the creator of LSTM networks, covering regression, classification, and ensemble methods.

Sepp Hochreiter

Practical guide to making ML models explainable, covering SHAP, LIME, partial dependence plots, and feature importance methods.

Christoph Molnar

Classic Stanford introduction covering decision trees, neural networks, Bayesian learning, and instance-based methods with clear explanations.

Nils J. Nilsson

Undergraduate thesis covering ML fundamentals with implementations in Python, suitable for beginners entering the field.

William J. Deuschle