Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics that are not normally included in introductory machine learning texts. All learning algorithms are described so that students can easily move from the equation in the book to a computer program. This text covers topics such as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, layered perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. It will also be interesting to engineers in the field who are concerned with the application of machine learning methods. Adaptive Computation and Machine Learning series