Foundations of Machine Learning, 2nd Edition
Price  $54.38  $68.05

eBook  Free 
Rating  
Authors  Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar 
Publisher  Selfpublishing 
Published  2018 
Pages  505 
Language  English 
Format  Paper book / ebook (PDF) 
ISBN10  0262039400 
ISBN13  9780262039406 
A new edition of a graduatelevel 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 selfcontained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VCdimension; Support Vector Machines (SVMs); kernel methods; boosting; online learning; multiclass 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 2nd 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
 Afshin Rostamizadeh
 Ameet Talwalkar
4 5 87
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