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An Introduction to Machine Learning

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Cover of 'An Introduction to Machine Learning'

Table of Contents

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    Book Overview
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    Chapter 1 A Simple Machine-Learning Task
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    Chapter 2 Probabilities: Bayesian Classifiers
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    Chapter 3 Similarities: Nearest-Neighbor Classifiers
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    Chapter 4 Inter-Class Boundaries: Linear and Polynomial Classifiers
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    Chapter 5 Artificial Neural Networks
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    Chapter 6 Decision Trees
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    Chapter 7 Computational Learning Theory
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    Chapter 8 A Few Instructive Applications
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    Chapter 9 Induction of Voting Assemblies
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    Chapter 10 Some Practical Aspects to Know About
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    Chapter 11 Performance Evaluation
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    Chapter 12 Statistical Significance
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    Chapter 13 Induction in Multi-Label Domains
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    Chapter 14 Unsupervised Learning
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    Chapter 15 Classifiers in the Form of Rulesets
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    Chapter 16 The Genetic Algorithm
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    Chapter 17 Reinforcement Learning
Attention for Chapter 17: Reinforcement Learning
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