An Introduction to Statistical Learning
Winner of the 2014 Eric Ziegel award from Technometrics.
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.
The book has been translated into Chinese, Italian, Japanese, Korean, Mongolian, Russian and Vietnamese.
The First Edition topics include:
Sparse methods for classification and regression
Decision trees
Boosting
Support vector machines
Clustering
The Second Edition adds:
Deep learning
Survival analysis
Multiple testing
Naive Bayes and generalized linear models
Bayesian additive regression trees
Matrix completion
Authors
E. Morgan Stanley Chair in Business Administration
Professor of Data Sciences and Operations
University of Southern California
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
Get the book
Due to a global paper shortage, there may be a delay in receiving your hard copy of the Second Edition.
