Foundations of Machine Learning

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

Overview of core ML concepts including supervised, unsupervised, and reinforcement learning with practical examples and algorithm comparisons.

Jaydip Sen

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

William J. Deuschle