Understanding Machine Learning

From Theory to Algorithms

by Shai Shalev-Shwartz, Shai Ben-David

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Book Description

The subject of this book is automated learning, or, as we will more often call it, Machine Learning (ML). That is, we wish to program computers so that they can "learn" from input available to them. Roughly speaking, learning is the process of converting experience into expertise or knowledge. The input to a learning algorithm is training data, representing experience, and the output is some expertise, which usually takes the form of another computer program that can perform some task. Seeking a formal-mathematical understanding of this concept, we'll have to be more explicit about what we mean by each of the involved terms: What is the training data our programs will access? How can the process of learning be automated? How can we evaluate the success of such a process (namely, the quality of the output of a learning program)?

This open book is licensed strictly for personal or educational use. You can download Understanding Machine Learning ebook for free in PDF format (3.5 MB).

Table of Contents

Chapter 1
Introduction
 
Part I
Foundations
 
Chapter 2
A Gentle Start
 
Chapter 3
A Formal Learning Model
 
Chapter 4
Learning via Uniform Convergence
 
Chapter 5
The Bias-Complexity Tradeoff
 
Chapter 6
The VC-Dimension
 
Chapter 7
Nonuniform Learnability
 
Chapter 8
The Runtime of Learning
 
Part II
From Theory to Algorithms
 
Chapter 9
Linear Predictors
 
Chapter 10
Boosting
 
Chapter 11
Model Selection and Validation
 
Chapter 12
Convex Learning Problems
 
Chapter 13
Regularization and Stability
 
Chapter 14
Stochastic Gradient Descent
 
Chapter 15
Support Vector Machines
 
Chapter 16
Kernel Methods
 
Chapter 17
Multiclass, Ranking, and Complex Prediction Problems
 
Chapter 18
Decision Trees
 
Chapter 19
Nearest Neighbor
 
Chapter 20
Neural Networks
 
Part III
Additional Learning Models
 
Chapter 21
Online Learning
 
Chapter 22
Clustering
 
Chapter 23
Dimensionality Reduction
 
Chapter 24
Generative Models
 
Chapter 25
Feature Selection and Generation
 
Part IV
Advanced Theory
 
Chapter 26
Rademacher Complexities
 
Chapter 27
Covering Numbers
 
Chapter 28
Proof of the Fundamental Theorem of Learning Theory
 
Chapter 29
Multiclass Learnability
 
Chapter 30
Compression Bounds
 
Chapter 31
PAC-Bayes
 
Appendix A
Technical Lemmas
 
Appendix B
Measure Concentration
 
Appendix C
Linear Algebra
 

Book Details

Subject
Computer Science
Publisher
Cambridge University Press
Published
2014
Pages
449
Edition
1
Language
English
ISBN13 Digital
9781107057135
ISBN10 Digital
1107057132
PDF Size
3.5 MB
License
For personal or educational use

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