Foundations of Machine Learning
Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar
Rigorous treatment of ML foundations covering PAC learning, Rademacher complexity, boosting, and kernel methods. Ideal for readers with strong mathematical background.
Loading the book...
Keep reading
-
Next in this topic
Machine Learning - Supervised TechniquesComprehensive 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.
Verified PDF · Secure download - Undergraduate Fundamentals of Machine Learning
Undergraduate thesis covering ML fundamentals with implementations in Python, suitable for beginners entering the field.
Verified PDF · Secure download