Introduction to Machine Learning
MIT Press, 22.08.2014. - 640 страница
A substantially revised third edition of a comprehensive textbook that covers a broad range of topics not often included in introductory texts.
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 machine learning exist already, including systems that analyze past sales data to predict customer behavior, 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. 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 statistical testing.
Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.
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2 Supervised Learning
3 Bayesian Decision Theory
4 Parametric Methods
5 Multivariate Methods
6 Dimensionality Reduction
8 Nonparametric Methods
12 Local Models
13 Kernel Machines
14 Graphical Models
15 Hidden Markov Models
16 Bayesian Estimation
17 Combining Multiple Learners
18 Reinforcement Learning
19 Design and Analysis of Machine Learning Experiments