Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Hidden Markov models -- Assessing and comparing classification algorithms -- Combining multiple learners -- Reinforcement learning -- Probability.

The goal of machine learning is to program computers to optimize a performance criterion using example data or past experience. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict consumer behaviour, recognize faces or spoken speech, optimize robot behaviour, etc so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. This is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. It discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial Intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The book is intended for senior graduate and post graduate level courses on machine learning and it should be of great interest to engineers working in the field concerned with the application of machine learning methods . The prerequisites are courses on computer programming, probability, calculus and linear algebra.