دانلود کتاب Foundations of Machine Learning

Mohri Mehryar, Afshin
Rostamizadeh, and Ameet Talwalkar

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.