Foundations of Machine Learning second edition

This book PDF is perfect for those who love Computers genre, written by Mehryar Mohri and published by MIT Press which was released on 25 December 2018 with total hardcover pages 505. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Foundations of Machine Learning second edition books below.

Foundations of Machine Learning  second edition
Author : Mehryar Mohri
File Size : 45,7 Mb
Publisher : MIT Press
Language : English
Release Date : 25 December 2018
ISBN : 9780262039406
Pages : 505 pages
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Foundations of Machine Learning second edition by Mehryar Mohri Book PDF Summary

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.

Foundations of Machine Learning  second edition

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