Foundations of Machine Learning - second edition (Hardcover)

Condition: Good
Seller notes: “This book is in a good condition.”
Publication Name: Foundations of Machine Learning second edition
Publisher: The MIT Press
Subject: Computer Science
Publication Year: 2018
Series: Adaptive Computation and Machine Learning series
Type: Textbook
Format: Hardcover
Language: English
Author: Mehryar MOHRI, Afshin ROSTAMIZADEH & Ameet TALWALKAR
Educational Level: Adult & Further Education
Level: Advanced
Country of Origin: United States
Item Height: 23.5cm
Item Length: 18.4cm
Item Width: 3.2cm
Item Weight: 1.24kg
Number of Pages: 488
ISBN: 9780262039406

Adaptive Computation and Machine Learning series

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.

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.

“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.”
~Peter Bartlett, Professor of Computer Science, University of California, Berkeley

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