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Book Description
This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting,” how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms.
This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.
Table of Contents
Chapter 1 A Simple Machine-Learning Task
Chapter 2 Probabilities: Bayesian Classifiers
Chapter 3 Similarities: Nearest-Neighbor Classifiers
Chapter 4 Inter-Class Boundaries: Linear And Polynomial Classifiers
Chapter 5 Artificial Neural Networks
Chapter 6 Decision Trees
Chapter 7 Computational Learning Theory
Chapter 8 A Few Instructive Applications
Chapter 9 Induction Of Voting Assemblies
Chapter 10 Some Practical Aspects To Know About
Chapter 11 Performance Evaluation
Chapter 12 Statistical Significance
Chapter 13 Induction In Multi-Label Domains
Chapter 14 Unsupervised Learning
Chapter 15 Classifiers In The Form Of Rulesets
Chapter 16 The Genetic Algorithm
Chapter 17 Reinforcement Learning
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