The goal of machine learning is to program computers to use example data or pastexperience to solve a given problem. Many successful applications of machine learning exist already,including systems that analyze past sales data to predict customer behavior, optimize robot behaviorso that a task can be completed using minimum resources, and extract knowledge from bioinformaticsdata. Introduction to Machine Learning is a comprehensive textbook on thesubject, covering a broad array of topics not usually included in introductory machine learningtexts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric,and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning;kernel machines; graphical models; Bayesian estimation; and statisticaltesting. Machine learning is rapidly becoming a skill that computer sciencestudents must master before graduation. The third edition of Introduction to MachineLearning reflects this shift, with added support for beginners, including selectedsolutions for exercises and additional example data sets (with code available online). Othersubstantial changes include discussions of outlier detection; ranking algorithms for perceptrons andsupport vector machines; matrix decomposition and spectral methods; distance estimation; new kernelalgorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesianmethods. All learning algorithms are explained so that students can easily move from the equationsin the book to a computer program. The book can be used by both advanced undergraduates and graduatestudents. It will also be of interest to professionals who are concerned with the application ofmachine learning methods.