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.

• R
• R software
• data mining  inference
• statistical learning
• supervised learning
• unsupervised learning

Front Matter

Introduction

Statistical Learning

Linear Regression

Classification

Resampling Methods

Linear Model Selection and Regularization

Moving Beyond Linearity

Tree-Based Methods

Support Vector Machines

Deep Learning

Survival Analysis and Censored Data

Unsupervised Learning

Multiple Testing

Back Matter

Department of Data Science and Operations, University of Southern California, Los Angeles, USA

Gareth James

Department of Statistics, University of Washington, Seattle, USA

Daniela Witten

Department of Statistics, Stanford University, Stanford, USA

Trevor Hastie, Robert Tibshirani

Book Title

An Introduction to Statistical Learning

Book Subtitle

with Applications in R

Authors

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

DOI：https://doi.org/10.1007/978-1-0716-1418-1

eBook Packages：Mathematics and Statistics, Mathematics and Statistics (R0)

Edition Number：2

Number of Pages：XV, 607

Number of Illustrations：9 b/w illustrations, 182 illustrations in colour

Topics:Statistical Theory and Methods, Statistics and Computing/Statistics Programs, Artificial Intelligence, Statistics, general