FOUNDATIONS OF MACHINE LEARNING, SECOND EDITION

Mehryar Mohri,Afshin Rostamizadeh and Ameet Talwalkar

ISBN 10: 0262039400 / ISBN 13: 9780262039406

Verlag: The MIT Press, 2018

Beschreibung

This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide. Bestandsnummer des Verkäufers ABNR-15237

Bibliografische Details

Titel: FOUNDATIONS OF MACHINE LEARNING, SECOND ...

Verlag: The MIT Press

Erscheinungsdatum: 2018

Einband: Hardcover

Zustand: New

Auflage: 2. Auflage

Über diesen Titel

Inhaltsangabe:

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.

Über die Autorin bzw. den Autor:

Mehryar Mohri is Professor of Computer Science at New York University's Courant Institute of Mathematical Sciences and a Research Consultant at Google Research.

Afshin Rostamizadeh is a Research Scientist at Google Research.

Ameet Talwalkar is Assistant Professor in the Machine Learning Department at Carnegie Mellon University.