An introduction to statistical learning: with applications in R

By:

James, Gareth

Contributor(s):

Material type: Text

TextPublication details: Springer New York 2021Edition: 2ndDescription: xv, 607 pISBN:

9781071614174

Subject(s):

DDC classification:

519.5 JAM

Summary: Presents an essential statistical learning toolkit for practitioners in science, industry, and other fields Demonstrates application of the statistical learning methods in R Includes new chapters on deep learning, survival analysis, and multiple testing Covers a range of topics, such as linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and deep learning Features extensive color graphics for a dynamic learning experience

List(s) this item appears in: Operation & quantitative Techniques

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Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Operations Management & Quantitative Techniques	519.5 JAM (Browse shelf(Opens below))	1	Available		001634
Book	Indian Institute of Management LRC General Stacks	Operations Management & Quantitative Techniques	519.5 JAM (Browse shelf(Opens below))	2	Available		001635

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Previous	519.5 HOY Handbook of structural equation modeling	519.5 JAG Business statistics: communicating with numbers	519.5 JAM An introduction to statistical learning: with applications in R	519.5 JAM An introduction to statistical learning: with applications in R	519.5 KOL An introduction to nonparametric statistics	519.5 KRI Statistics and data analysis for social science	519.5 LED Fundamentals of high-dimensional statistics: with exercises and R labs	Next

Table of content

Front Matter
Pages i-xv
PDF
Introduction
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Pages 1-14
Statistical Learning
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Pages 15-57
Linear Regression
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Pages 59-128
Classification
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Pages 129-195
Resampling Methods
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Pages 197-223
Linear Model Selection and Regularization
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Pages 225-288
Moving Beyond Linearity
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Pages 289-326
Tree-Based Methods
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Pages 327-365
Support Vector Machines
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Pages 367-402
Deep Learning
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Pages 403-460
Survival Analysis and Censored Data
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Pages 461-495
Unsupervised Learning
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Pages 497-552
Multiple Testing
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Pages 553-595

Presents an essential statistical learning toolkit for practitioners in science, industry, and other fields
Demonstrates application of the statistical learning methods in R
Includes new chapters on deep learning, survival analysis, and multiple testing
Covers a range of topics, such as linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and deep learning
Features extensive color graphics for a dynamic learning experience

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