An Introduction to Statistical Learning with Applications in R Free PDF

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An Introduction to Statistical Learning with Applications in R Free PDF Book Details

  • Book Title: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
  • Edition Description: 1st ed. 2013, Corr. 7th printing 2017
  • Author: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
  • Publication Date: 09/01/2017
  • ISBN: 9781461471370
  • Formats: PDF
  • No. of pages: 426
  • Size: 11 MB
  • Genre: Textbook
  • Language: English
  • File Status: Available for Download
  • Price: Free

About An Introduction to Statistical Learning with Applications in R

This book presents key modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering.

From the Publisher

“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)

Table of Contents For An Introduction to Statistical Learning with Applications in R eBook

Preface.-

1 Introduction.-

2 Statistical Learning.-

3 Linear Regression.-

4 Classification.-

5 Resampling Methods.-

6 Linear Model Selection and Regularization.-

7 Moving Beyond Linearity.-

8 Tree-Based Methods.-

9 Support Vector Machines.-

10 Deep Learning.-

11 Survival Analysis and Censored Data.-

12 Unsupervised Learning.

13 Multiple Testing.- Index.

About the Author of An Introduction to Statistical Learning with Applications in R PDF

Gareth James is a professor of data sciences and operations, and the E. Morgan Stanley Chair in Business Administration, 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 a professor of statistics and biostatistics, and the Dorothy Gilford Endowed Chair, at the University of Washington. Her research focuses largely on statistical machine learning techniques for the analysis of complex, messy, and large-scale data, 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 

An Introduction to Statistical Learning with Applications in R Free PDF

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