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
Part I
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
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
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
Appendix A
Technical Lemmas
Appendix B
Measure Concentration
Appendix C
Linear Algebra

Book Details

Computer Science
Cambridge University Press
ISBN13 Digital
ISBN10 Digital
PDF Size
3.5 MB
For personal or educational use

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