Artificial Intelligence

Foundations Of Machine Learning Hardcover English by Mehryar Mohri - 43459

431.30

Inclusive of VAT

Only 3 left in stock

Get it by 14 June

Order in 20 h 42 m

Delivery by noon

High Rated Seller

Cash on Delivery

Secure Transaction

BANK OFFERS

Earn  21.57 cashback with the Mashreq noon Credit Card.

SELLER

Item as shown

Partner since

Free delivery on Lockers & Pickup Points

This item is eligible for free returns

Product Overview

Specifications

Publisher	Mit Press Ltd
ISBN 13	9780262039406
ISBN 10	262039400
Author	Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar
Book Format	Hardcover
Language	English

Book Description	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.
About the Author	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.
Language	English
Edition Number	2
Publication Date	43459
Number of Pages	504

Cart Total  431.30