**by Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar**

DescriptionTable of ContentsDetailsHashtagsReport an issue ### 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 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. ### Table of Contents

### Book Details

### Related Books

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 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.

This open book is licensed under a Creative Commons License (CC BY-NC-ND). You can download Foundations of Machine Learning ebook for free in PDF format (6.8 MB).

Chapter 1

Introduction

Chapter 2

The PAC Learning Framework

Chapter 3

Rademacher Complexity and VC-Dimension

Chapter 4

Model Selection

Chapter 5

Support Vector Machines

Chapter 6

Kernel Methods

Chapter 7

Boosting

Chapter 8

On-Line Learning

Chapter 9

Multi-Class Classification

Chapter 10

Ranking

Chapter 11

Regression

Chapter 12

Maximum Entropy Models

Chapter 13

Conditional Maximum Entropy Models

Chapter 14

Algorithmic Stability

Chapter 15

Dimensionality Reduction

Chapter 16

Learning Automata and Languages

Chapter 17

Reinforcement Learning

Title

Foundations of Machine Learning

Subject

Computer Science

Publisher

MIT Press

Published

2018

Pages

505

Edition

2

Language

English

ISBN13 Digital

9780262039406

ISBN10 Digital

0262039400

PDF Size

6.8 MB

License

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