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An Introduction to Statistical Learning: with Applications in R

Author(s): Gareth James1, Daniela Witten2, Trevor Hastie3, Robert Tibshirani3

1. University of Southern California 2. University of Washington 3. Stanford University

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Summary:
This book provides an introduction to statistical learning methods and is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences.

Licensed under these terms

Version 1.0 - published on 23 Oct 2018 doi:10.25334/Q4HT55 - cite this

Keywords

  1. linear regression
  2. textbook
  3. classification
  4. course
  5. clustering coefficient
  6. R programming language
  7. statistical learning
  8. machine learning
  9. logistic regression
  10. resampling
  11. model selection
  12. Generalized additive models
  13. decision trees
  14. principal components analysis
  15. MOOC
  16. Resources @ QUBES - Teaching & Reference

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Description

From the book website:

This book provides an introduction to statistical learning methods. It is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences. The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist.

For a more advanced treatment of these topics: The Elements of Statistical Learning.

Slides and videos for Statistical Learning MOOC by Hastie and Tibshirani available separately here. Slides and video tutorials related to this book by Abass Al Sharif can be downloaded here.

"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 Department of Machine Learning, CMU.

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