This textbook offers a comprehensive introduction to Machine Learning techniques and algorithms. This Third Edition covers newer approaches that have become highly topical, including deep learning, and auto-encoding, introductory information about temporal learning and hidden Markov models, and a much more detailed treatment of reinforcement learning. The book is written in an easy-to-understand manner with many examples and pictures, and with a lot of practical advice and discussions of simple applications.
The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, rule-induction programs, artificial neural networks, support vector machines, boosting algorithms, unsupervised learning (including Kohonen networks and auto-encoding), deep learning, reinforcement learning, temporal learning (including long short-term memory), hidden Markov models, and the genetic algorithm. Special attention is devoted to performance evaluation, statistical assessment, and to many practical issues ranging from feature selection and feature construction to bias, context, multi-label domains, and the problem of imbalanced classes.
Cover Front Matter 1. Ambitions and Goals of Machine Learning 2. Probabilities: Bayesian Classifiers 3. Similarities: Nearest-Neighbor Classifiers 4. Inter-Class Boundaries: Linear and Polynomial Classifiers 5. Decision Trees 6. Artificial Neural Networks 7. Computational Learning Theory 8. Experience from Historical Applications 9. Voting Assemblies and Boosting 10. Classifiers in the Form of Rule-Sets 11. Practical Issues to Know About 12. Performance Evaluation 13. Statistical Significance 14. Induction in Multi-label Domains 15. Unsupervised Learning 16. Deep Learning 17. Reinforcement Learning: N-Armed Bandits and Episodes 18. Reinforcement Learning: From TD(0) to Deep-Q-Learning 19. Temporal Learning 20. Hidden Markov Models 21. Genetic Algorithm Back Matter
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