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
Dorothy Gilford Endowed Chair
Professor of Statistics
Professor of Biostatistics
University of Washington
The John A. Overdeck Professor
Professor of Statistics
Professor of Biomedical Data Science
Stanford University