“An Introduction to Statistical Learning” offers a user-friendly introduction to the discipline of statistical learning, a fundamental toolkit for interpreting the extensive and intricate datasets that have emerged across disciplines such as biology, finance, marketing, and astrophysics over the past two decades. The book presents a selection of the most significant modeling and forecasting strategies, along with their practical applications. It delves into subjects such as linear regression, classification algorithms, resampling techniques, regularization methods, decision trees, support vector machines, clustering algorithms, deep learning, survival analysis, multiple hypothesis testing, and more. The book employs colorful graphics and real-world examples to clarify the presented methods.

The primary objective of this textbook is to enable professionals from various scientific, industrial, and other disciplines to apply statistical learning methods effectively. Each chapter includes a tutorial on how to perform the analyses and techniques discussed using R, a widely-used, open-source statistical software platform.

Two of the book’s authors are also the co-authors of “The Elements of Statistical Learning” (Hastie, Tibshirani, and Friedman, 2nd edition, 2009), a widely respected reference for statisticians and machine learning researchers. While “An Introduction to Statistical Learning” covers many of the same topics, it is designed to be more accessible to a wider audience. It assumes that readers have taken a course in linear regression but no prior knowledge of matrix algebra is required.

The second edition of the book introduces new chapters on deep learning, survival analysis, and multiple testing, while also extending discussions on topics like naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. The R code has been updated throughout the book to ensure compatibility with the latest software versions.