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 autoencoding introductory information about temporal learning and hidden Markov models and a much more detailed treatment of reinforcement learning. The book is written in an easytounderstand manner with many examples and pictures and with a lot of practical advice and discussions of simple applications.ampampnbspThe main topics include Bayesian classifiers nearestneighbor classifiers linear and polynomial classifiers decision trees ruleinduction programs artificial neural networks support vector machines boosting algorithms unsupervised learning including Kohonen networks and autoencoding deep learning reinforcement learning temporal learning including long shortterm memory hidden Markov models and the genetic algorithm.