Mathematical Background for Machine Learning
Garrett Thomas
Concise Berkeley CS 189 review covering linear algebra, probability, and calculus essentials needed before studying ML.
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Foundations of Machine LearningRigorous treatment of ML foundations covering PAC learning, Rademacher complexity, boosting, and kernel methods. Ideal for readers with strong mathematical background.
Verified PDF · Secure download - Machine Learning - Supervised Techniques
Comprehensive lecture notes on supervised ML techniques from the creator of LSTM networks, covering regression, classification, and ensemble methods.
Verified PDF · Secure download - Interpretable Machine Learning
Practical guide to making ML models explainable, covering SHAP, LIME, partial dependence plots, and feature importance methods.
Verified PDF · Secure download - Introduction to machine learning
Classic Stanford introduction covering decision trees, neural networks, Bayesian learning, and instance-based methods with clear explanations.
Verified PDF · Secure download - Machine Learning
Overview of core ML concepts including supervised, unsupervised, and reinforcement learning with practical examples and algorithm comparisons.
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