Introduction to Machine Learning (2014) Summary An

文件名称: Introduction to Machine Learning (2014)

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详细说明： Summary An introductory text in machine learning that gives a unified treatment of methods based on statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. The goal of machine learning is to program computers to use example data or past experience to solve a given prob lem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning 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 based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The book can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest 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, dimensionality reduction, clustering, nonparametric methods, decision trees, linear discrimination, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, combining multiple learners, and reinforcement learning. Brief Contents 1 Introduction 1 2 Supervised Learning 21 3 Bayesian Decision Theory 49 4 Parametric Methods 65 5 Multivariate Methods 93 6 Dimensionality Reduction 115 7 Clustering 161 8 Nonparametric Methods 185 9 Decision Trees 213 10 Linear Discrimination 239 11 Multilayer Perceptrons 267 12 Local Models 317 13 Kernel Machines 349 14 Graphical Models 387 15 Hidden Markov Models 417 16 Bayesian Estimation 445 17 Combining Multiple Learners 487 18 Reinforcement Learning 517 19 Design and Analysis of Machine Learning Experiments 547 A Probability 593

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