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An Introduction to Statistical Learning: with Applications in R Hardcover – July 30 2021

by Gareth James (Author), Daniela Witten (Author), Trevor Hastie (Author),
4.7 4.7 out of 5 stars 214 ratings
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  1. ISBN-10
    1071614177
  2. ISBN-13
    978-1071614174
  3. Edition
    2nd ed. 2021
  4. Publisher
    Springer
  5. Publication date
    July 30 2021
  6. Part of series
    Springer Texts in Statistics
  7. Language
    English
  8. Dimensions
    20.32 x 3.05 x 23.88 cm
  9. Print length
    607 pages
  10. See all details
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Product description

Review

"An Introduction to Statistical Learning (ISL)" by James, Witten, Hastie and Tibshirani is the "how to'' manual for statistical learning. Inspired by "The Elements of Statistical Learning'' (Hastie, Tibshirani and Friedman), this book provides clear and intuitive guidance on how to implement cutting edge statistical and machine learning methods. ISL makes modern methods accessible to a wide audience without requiring a background in Statistics or Computer Science. The authors give precise, practical explanations of what methods are available, and when to use them, including explicit R code. Anyone who wants to intelligently analyze complex data should own this book." (Larry Wasserman, Professor, Department of Statistics and Machine Learning Department, Carnegie Mellon University)

From the Back Cover

An Introduction to Statistical Learning  provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.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.This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

Product details

  • Publisher ‏ : ‎ Springer; 2nd ed. 2021 edition (July 30 2021)
  • Language ‏ : ‎ English
  • Hardcover ‏ : ‎ 607 pages
  • ISBN-10 ‏ : ‎ 1071614177
  • ISBN-13 ‏ : ‎ 978-1071614174
  • Item weight ‏ : ‎ 1.19 kg
  • Dimensions ‏ : ‎ 20.32 x 3.05 x 23.88 cm
  • Customer Reviews:
    4.7 4.7 out of 5 stars 214 ratings

About the authors

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  1. Daniela Witten

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    Daniela Witten is a professor of Statistics and Biostatistics at University of Washington, and the Dorothy Gilford Endowed Chair. She is the recipient of a NIH Director's Early Independence Award, a NSF CAREER Award, a Sloan Research Fellowship, and a Simons Investigator Award. For more, see www.danielawitten.com

    See more on the author's page

  2. Robert Tibshirani

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    Robert Tibshirani (born July 10, 1956) is a Professor in the Departments of Statistics and Health Research and Policy at Stanford University. He was a Professor at the University of Toronto from 1985 to 1998. In his work, he develops statistical tools for the analysis of complex datasets, most recently in genomics and proteomics.

    His most well-known contributions are the LASSO method, which proposed the use of L1 penalization in regression and related problems, and Significance Analysis of Microarrays. He has also co-authored three well-known books: "Generalized Additive Models", "An Introduction to the Bootstrap", and "The Elements of Statistical Learning", the last of which is available for free from the author's website.

    Bio from Wikipedia, the free encyclopedia. Photo by Tibshirani (i took this photo) [GFDL (http://www.gnu.org/copyleft/fdl.html) or CC BY-SA 3.0 (http://creativecommons.org/licenses/by-sa/3.0)], via Wikimedia Commons.

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  3. Trevor Hastie

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    Full content visible, double tap to read brief content.

    Trevor Hastie is the John A Overdeck Professor of Statistics at

    Stanford University. Hastie is known for his research in applied

    statistics, particularly in the fields of statistical modeling, bioinformatics

    and machine learning. He has published six books and over 200

    research articles in these areas. Prior to joining Stanford

    University in 1994, Hastie worked at AT&T Bell Laboratories for nine

    years, where he contributed to the development of the statistical modeling environment

    popular in the R computing system. He received a B.Sc. (hons) in statistics

    from Rhodes University in 1976, a M.Sc. from the University of Cape

    Town in 1979, and a Ph.D from Stanford in 1984. In 2018 he was elected

    to the U.S. National Academy of Sciences. He is a dual citizen of the

    United States and South Africa.

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  4. Gareth James

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    Discover more of the author’s books, see similar authors, read author blogs and more

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Customer reviews

4.7 out of 5 stars
4.7 out of 5
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