Introduction to Statistical Learning Second Edition

The second edition of the classic "An Introduction to Statistical Learning, with Applications in R" was published very recently, and is now freely-available via PDF on the book's website.

An Introduction to Statistical Learning, with Applications in R, written by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, is an absolute classic in the space. The book, a staple of statistical learning texts, is accessible to readers of all levels, and can be read without much of an existing foundational knowledge in the area.

While the original has been around since 2013, the second edition was published very recently, and is now freely-available via PDF on the book's website.


A description, directly from the books' website:

As the scale and scope of data collection continue to increase across virtually all fields, statistical learning has become a critical toolkit for anyone who wishes to understand data. An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. Each chapter includes an R lab. This book is appropriate for anyone who wishes to use contemporary tools for data analysis.


Topics highlighted originally from the first edition include:

  • Sparse methods for classification and regression
  • Decision trees
  • Boosting
  • Support vector machines
  • Clustering

The second edition has been expanded to include the following topics of note:

  • Deep learning
  • Survival analysis
  • Multiple testing
  • Naive Bayes and generalized linear models
  • Bayesian additive regression trees
  • Matrix completion

An Introduction to Statistical Learning, with Applications in R (ISLR) can be considered a less advanced treatment of the topics found in another classic of the genre written by some of the same authors, The Elements of Statistical Learning. Another major difference between these 2 titles, beyond the level of depth of the material covered, is that ISLR introduces these topics alongside practical implementations in a programming language, in this case R.

As mentioned above, the book is an absolute classic in the genre. But you don't need to take my word for how essential of a text it is. Here's a review (taken from the book's Amazon site) by Larry Wasserman of Carnegie Mellon University:

"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


The print copy of the book can be purchased on Amazon, while the PDF can be downloaded freely from here.

Accompanying code and datasets can be found here.