An introduction to statistical learning : with applications in R
 Language
 English.
 Publication
 New York : Springer, [2013]
 Copyright notice
 ©2013
 Physical description
 xiv, 426 pages : illustrations (some color) ; 24 cm
 Series
 Springer texts in statistics ; 103.
Access
Available online
Course reserve
 Course
 STATS20201  Data Mining and Analysis
 Instructor(s)
 Mackey, Lester Wayne

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QA276 .I585 2013
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Contributors
 Contributor
 James, Gareth author.
 Witten, Daniela author.
 Hastie, Trevor, author.
 Tibshirani, Robert, author.
Contents/Summary
 Contents

 Introduction. Statistical Learning. Linear Regression. Classification. Resampling Methods. Linear Model Selection and Regularization. Moving Beyond Linearity. TreeBased Methods. Support Vector Machines. Unsupervised Learning. Index.
 (source: Nielsen Book Data)
 Publisher's Summary
 An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, treebased methods, support vector machines, clustering, and more. Color graphics and realworld examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors cowrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and nonstatisticians alike who wish to use cuttingedge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
(source: Nielsen Book Data)
Subjects
Bibliographic information
 Publication date
 2013
 Responsibility
 Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.
 Title Variation
 Statistical learning
 Series
 Springer texts in statistics, 1431875X ; 103
 Note
 Includes index.
 ISBN
 9781461471370 (acidfree paper)
 1461471370 (acidfree paper)
 9781461471387 (eBook)
 1461471389 (eBook)