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.
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"It is superbly organized: each section includes a 'what have you learned' summary, and every chapter has a short summary, accompanying (brief) historical remarks, and a slew of exercises. ... In most of the chapters, there are very clear examples, well chosen and illustrated, that really help the reader understand each concept. ... I did learn quite a bit about very basic machine learning by reading this book." (Jacques Carette, Computing Reviews, January, 2016)