Introduction to Machine Learning

摘要: The goal of machine learning is to program computers use example data or past experience solve a given problem. Many successful applications exist already, including systems that analyze sales predict customer behavior, optimize robot behavior so task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction Machine Learning comprehensive textbook on the subject, covering broad array topics not usually included in introductory texts. In order present unified treatment problems solutions, it discusses many methods different fields, statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, mining. All algorithms are explained student easily move equations book computer program. text covers such as supervised learning, Bayesian decision theory, parametric methods, multivariate multilayer perceptrons, local models, hidden Markov assessing comparing classification algorithms, reinforcement learning. New second edition chapters kernel machines, graphical estimation; expanded coverage statistical tests chapter design analysis experiments; case studies available Web (with downloadable results for instructors); additional exercises. have been revised updated. used by advanced undergraduates graduate students who courses programming, probability, calculus, linear algebra. It will also interest engineers field concerned with application methods. Adaptive Computation series