Understanding Machine Learning

From Theory to Algorithms

Shai Shalev-Shwartz, Shai Ben-David

• Publisher: Cambridge University Press
• Year: 2014
• Print ISBN: 9781107057135
• eBook ISBN: 9781139950619
• Topic: Computer Science
• Subtopic: Computer Vision & Pattern Recognition
• Index: Computer Science

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.

Table of contents

• Cover
• Half-title
• Title page
• Copyright information
• Dedication
• Table of contents
• Preface
• 1 Introduction
• Part 1 Foundations
• Part 2 From Theory to Algorithms
• Part 3 Additional Learning Models
• Part 4 Advanced Theory
• References
• Index