An Introduction to Statistical Learning: With Applications in R – Second Edition

About the Book

An Introduction to Statistical Learning: With Applications in R (Second Edition) is an accessible and comprehensive textbook that introduces readers to the essential concepts and techniques of statistical learning. Written by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani, the book explains how statistical methods can be used to analyse data, identify patterns, and make accurate predictions.

The book covers a wide range of important topics, including linear regression, classification, resampling methods, model selection, regularization, tree-based methods, support vector machines, deep learning, survival analysis, and clustering. It combines theoretical explanations with practical examples and R programming applications to help readers understand and implement statistical learning techniques.

The second edition includes updated material and additional chapters covering deep learning, survival analysis, and multiple testing. Its practical approach makes it a valuable learning resource for students, researchers, data analysts, and professionals interested in statistics, machine learning, and data science.

Whether you are developing your statistical knowledge or strengthening your data analysis skills, this book provides a solid foundation for understanding and applying modern statistical learning methods.

About the Authors

Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani are distinguished academics and researchers renowned for their contributions to statistics, data science, and statistical learning.

• Gareth James is a Professor of Data Sciences and Operations at the University of Southern California, specialising in statistical learning and data analytics.
• Daniela Witten is a Professor of Statistics and Biostatistics at the University of Washington, known for her research in statistical machine learning and high-dimensional data analysis.
• Trevor Hastie is a Professor of Statistics at Stanford University, recognised for his contributions to statistical modelling, machine learning, and computational statistics.
• Robert Tibshirani is a Professor of Statistics at Stanford University, widely known for developing the Lasso method and advancing modern statistical learning.

Together, their academic expertise and research experience make their books valuable resources for students, researchers, and professionals interested in statistics, machine learning, and data science.