Foundations Of Machine Learning, 2Nd Edn

This graduate-level machine learning textbook centers on the analysis and theory of algorithms. It provides a rigorous, broad introduction to machine learning designed for graduate students and researchers who want a solid theoretical foundation alongside practical insight into algorithm design and justification. The tone is focused, precise, and encouraging, with concise proofs and accessible explanations that help readers build confidence while tackling advanced topics. Content is presented in largely self-contained chapters that begin from foundational ideas and move through key topics in modern machine learning. The material foregrounds the PAC learning framework and generalization bounds, then explores VC-dimension, Rademacher complexity, and core algorithms such as Support Vector Machines, kernel methods, boosting, and online learning. It covers multi-class classification, ranking, regression, algorithmic stability, dimensionality reduction, learning automata and languages, and reinforcement learning, always tying theory to potential applications. Each chapter ends with exercises, and the appendices provide essential probability background. The second edition adds three new chapters on model selection, maximum entropy models, and conditional entropy models, as well as expanded appendices on Fenchel duality, concentration inequalities, and information theory. More than hal