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 Title An Introduction to Statistical Learning: with Applications in R
 Author(s) Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
 Publisher: Springer; 2nd ed. (2021); eBook (Online 2nd Ed., First printing, 2021)
 Hardcover 622 pages
 eBook PDF (612 pages)
 Language: English
 ISBN10: 1071614177
 ISBN13: 9781071614174
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Book Description
This book 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.
This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.
About the Authors Gareth James is the E. Morgan Stanley Chair in Business Administration and a professor of data sciences and operations at the Marshall School of Business at the University of Southern California.
 Daniela Witten is a professor of statistics and biostatistics at the University of Washington.
 Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University, and are coauthors of the successful textbook Elements of Statistical Learning.
 Machine Learning
 The R Programming Language
 Statistics, Mathematical Statistics, and SAS Programming
 Data Analysis and Data Mining
 Artificial Intelligence