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Foundations of Machine Learning

by Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar

Foundations of Machine Learning

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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.

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).

Table of Contents

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

Book Details

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
CC BY-NC-ND

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