An Introduction to Machine Learning, 2nd Edition下载

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