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, deep learning, survival analysis, multiple testing, 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 authors co-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.
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.
