Springer Science & Business Media

Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. **An Introduction to Statistical Learning** covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

**Gareth James** is a professor of data sciences and operations at the University of Southern California. He has published an extensive body of methodological work in the domain of statistical learning with particular emphasis on high-dimensional and functional data. The conceptual framework for this book grew out of his MBA elective courses in this area.

**Daniela Witten** is an associate professor of statistics and biostatistics at the University of Washington. Her research focuses largely on statistical machine learning in the high-dimensional setting, with an emphasis on unsupervised learning.

**Trevor Hastie** and **Robert Tibshirani **are professors of statistics at Stanford University, and are co-authors of the successful textbook Elements of Statistical Learning. Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap.

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Publisher

Springer Science & Business Media

Published on

Jun 24, 2013

Pages

426

ISBN

9781461471387

Features

Best For

Language

English

Genres

Computers / Intelligence (AI) & Semantics

Computers / Mathematical & Statistical Software

Mathematics / Probability & Statistics / General

Mathematics / Probability & Statistics / Stochastic Processes

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