Overview
- Provides tools for Statistical Learning that are essential for practitioners in science, industry and other fields
- Analyses and methods are presented in R
- Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering
- Extensive use of color graphics assist reader
- Includes supplementary material: sn.pub/extras
Part of the book series: Springer Texts in Statistics (STS, volume 103)
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Table of contents (10 chapters)
Keywords
About this book
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, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world 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 authorsco-wrote 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 non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
Reviews
"An Introduction to Statistical Learning (ISL)" by James, Witten, Hastie and Tibshirani is the "how to'' manual for statistical learning. Inspired by "The Elements of Statistical Learning'' (Hastie, Tibshirani and Friedman), this book provides clear and intuitive guidance on how to implement cutting edge statistical and machine learning methods. ISL makes modern methods accessible to a wide audience without requiring a background in Statistics or Computer Science. The authors give precise, practical explanations of what methods are available, and when to use them, including explicit R code. Anyone who wants to intelligently analyze complex data should own this book." (Larry Wasserman, Professor, Department of Statistics and Machine Learning Department, Carnegie Mellon University)
Authors and Affiliations
About the authors
Gareth James is a professor of data sciences and operations at the University of Southern California. He has published an extensive body of methodological work in the domain of statistical learning with particular emphasis on high-dimensional and functional data. The conceptual framework for this book grew out of his MBA elective courses in this area.
Daniela Witten is an associate professor of statistics and biostatistics at the University of Washington. Her research focuses largely on statistical machine learning in the high-dimensional setting, with an emphasis on unsupervised learning.
Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University, and are co-authors of the successful textbook Elements of Statistical Learning. Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environmentin R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap.
Bibliographic Information
Book Title: An Introduction to Statistical Learning
Book Subtitle: with Applications in R
Authors: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Series Title: Springer Texts in Statistics
DOI: https://doi.org/10.1007/978-1-4614-7138-7
Publisher: Springer New York, NY
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Science+Business Media, LLC, part of Springer Nature 2013
eBook ISBN: 978-1-4614-7138-7Published: 24 June 2013
Series ISSN: 1431-875X
Series E-ISSN: 2197-4136
Edition Number: 1
Number of Pages: XIV, 426
Number of Illustrations: 556 b/w illustrations
Topics: Statistical Theory and Methods, Statistics and Computing/Statistics Programs, Artificial Intelligence, Statistics, general
Industry Sectors: Aerospace, Biotechnology, Chemical Manufacturing, Energy, Utilities & Environment, Finance, Business & Banking, IT & Software, Oil, Gas & Geosciences, Pharma, Telecommunications