An Introduction to Statistical Learning: with Applications in R

An Introduction to Statistical Learning is a textbook that provides an accessible overview of the field of statistical learning, presenting important modeling and prediction techniques with relevant applications. It is targeted at practitioners in science, industry, and other fields and assumes only a previous course in linear regression. The Second Edition features new chapters on deep learning, survival analysis, and multiple testing, and updated R code.

Format: Paperback / softback
Length: 607 pages
Publication date: 30 July 2022
Publisher: Springer-Verlag New York Inc.

An Introduction to Statistical Learning offers a comprehensive and accessible overview of the field of statistical learning, a vital toolkit for making sense of the vast and intricate data sets that have emerged in diverse fields such as biology, finance, marketing, and astrophysics over the past two decades. This comprehensive textbook presents some of the most important modeling and prediction techniques, along with relevant applications. Topics covered include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. To illustrate the methods presented, color graphics and real-world examples are utilized.

Since the primary objective of this textbook is to facilitate the practical application of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter includes a tutorial on implementing the analyses and methods presented using R, an extremely popular open-source statistical software platform.

Two of the authors, namely Hastie, Tibshirani, and Friedman, co-wrote The Elements of Statistical Learning (Hastie, Tibshirani, and Friedman, 2nd edition, 2009), a widely recognized reference book for statistics and machine learning researchers. While An Introduction to Statistical Learning covers many of the same topics, it is designed to be accessible to a much broader audience. This book is aimed at statisticians and non-statisticians alike who wish to leverage cutting-edge statistical learning techniques to analyze their data. The text assumes a prior course in linear regression and no prior knowledge of matrix algebra.

In this Second Edition, several new chapters have been added, covering topics such as deep learning, survival analysis, and multiple testing. Additionally, the treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix algebra have been expanded and revised to provide a more comprehensive understanding of these subjects.

By providing a comprehensive and accessible introduction to statistical learning, An Introduction to Statistical Learning serves as a valuable resource for students, researchers, and practitioners seeking to leverage the power of data analysis in their respective fields.

Weight: 928g
Dimension: 156 x 233 x 35 (mm)
ISBN-13: 9781071614204
Edition number: 2nd ed. 2021