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
A Gentle Start
A Formal Learning Model
Learning via Uniform Convergence
The Bias-Complexity Tradeoff
The Runtime of Learning
From Theory to Algorithms
Model Selection and Validation
Convex Learning Problems
Regularization and Stability
Stochastic Gradient Descent
Support Vector Machines
Multiclass, Ranking, and Complex Prediction Problems
Additional Learning Models
Feature Selection and Generation
Proof of the Fundamental Theorem of Learning Theory
Understanding Machine Learning
Cambridge University Press
For personal or educational use
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