Foundations of Machine Learning by Mehryar Mohri

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Book Description:

This college-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical basis of these algorithms and illustrates the key aspects of their application. The authors aim to introduce new theoretical concepts and tools while providing concise evidence for even relatively advanced topics. Machine Learning Fundamentals fulfills the need for a general textbook that provides theoretical detail and emphasizes proof. Some topics that are often treated with insufficient attention are discussed in more detail here; for example, entire chapters are devoted to regression, multiclass classification, and classification. The first three chapters lay the theoretical foundation for what follows, but each remaining chapter is mostly self-contained. The appendix provides a brief overview of probability, a brief introduction to convex optimization, tools for concentration limits, and several basic properties of the matrices and norms used in the book.

The book is intended for graduate students and researchers in machine learning, statistics and related fields; It can be used as a textbook or as a reference text for a research seminar.

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 to discuss and justify 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 Learningis 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.

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Author(s): Mohri Mehryar, Afshin Rostamizadeh, and Ameet Talwalkar

Series: Adaptive Computation and Machine Learning

Publisher: The MIT Press, Year: 2018

ISBN: 0262039400; 978-0262039406

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