The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of learning machines already exist, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that tasks can be completed using minimum resources, and extract knowledge from bioinformatics data. It will also be interesting to engineers in the field who are concerned with the application of machine learning methods. After an introduction that defines machine learning and gives examples of machine learning applications, the book covers supervised learning, Bayesian decision theory, parametric methods, multivariate methods, dimension reduction, clustering, nonparametric methods, decision trees, linear discrimination, layered perceptrons, local models, hidden Markov model, assessing and comparing classification algorithms, combining multiple learners, and reinforcement learning.