An Introduction to Statistical Learning

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. This book is appropriate for anyone who wishes to use contemporary tools for data analysis.

The first edition of this book, with applications in R (ISLR), was released in 2013. A 2nd Edition of ISLR was published in 2021. It has been translated into Chinese, Italian, Japanese, Korean, Mongolian, Russian, and Vietnamese. The Python edition (ISLP) was published in 2023.

Each edition contains a lab at the end of each chapter, which demonstrates the chapter’s concepts in either R or Python.

The chapters cover the following topics:

  • What is statistical learning?

  • Regression

  • Classification

  • Resampling methods

  • Linear model selection and regularization

  • Moving beyond linearity

  • Tree-based methods

  • Support vector machines

  • Deep learning

  • Survival analysis

  • Unsupervised learning

  • Multiple testing

Authors


Gareth James

John H. Harland Dean
Goizueta Business School

Emory University


Daniela Witten

Dorothy Gilford Endowed Chair
Professor of Statistics
Professor of Biostatistics

University of Washington


Trevor Hastie

The John A. Overdeck Professor
Professor of Statistics
Professor of Biomedical Data Science

Stanford University


Rob Tibshirani

Professor of Biomedical Data Science
Professor of Statistics

Stanford University

A new team member for the Python edition:

Jonathan Taylor

Professor of Statistics

Stanford University

 

Get the book

Purchase ISL with Python here:

Purchase the 2nd Edition of ISL with R here:

Download the PDFs here: