Foundations of Machine Learning, Second Edition
A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms.
This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics.
Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review.
This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.
Downloadable instructor resources available for this title: slides, solutions, and file of figures in the book.
Hardcover$75.00 X ISBN: 9780262039406 504 pp. | 7 in x 9 in 64 color illus., 35 b&w illus.
“A clear, rigorous treatment of machine learning that covers a broad range of problems and methods from a theoretical perspective. This edition includes many updates, including new chapters on model selection and maximum entropy methods. It will be a standard graduate-level reference.”
Professor of Computer Science, University of California, Berkeley
"Foundations of Machine Learning is a neat and mathematically rigorous book providing broad coverage of basic and advanced topics in Machine Learning, but also a valuable textbook for graduate-level courses in the modern theory of Machine Learning. This book is unique in its content and style, a 'must-have' reference book for researchers and students."
Inria Lille and Google Research, New York
"I've found the first edition of this book to be a valuable resource in five or so years of teaching -- and look forward to using the much-improved and expanded second edition in future courses."
Associate Professor of Computer Science, Ben-Gurion University