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Springer Texts in Statistics Ser.: An Introduction to Statistical Learning : With Applications in R by Daniela Witten, Gareth James, Trevor Hastie and Robert Tibshirani (2021, Hardcover)

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This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. An Introduction to Statistical Learning: With Applications in R, HARDCOVER.

About this product

Product Information

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 na ve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

Product Identifiers

Publisher
Springer
ISBN-10
1071614177
ISBN-13
9781071614174
eBay Product ID (ePID)
17050082535

Product Key Features

Number of Pages
Xv, 607 Pages
Language
English
Publication Name
Introduction to Statistical Learning : with Applications in R
Publication Year
2021
Subject
Mathematical & Statistical Software, Probability & Statistics / General, Intelligence (Ai) & Semantics, General
Type
Textbook
Subject Area
Computers, Mathematics
Author
Trevor Hastie, Gareth James, Robert Tibshirani, Daniela Witten
Series
Springer Texts in Statistics Ser.
Format
Hardcover

Dimensions

Item Weight
42 Oz
Item Length
9.3 in
Item Width
6.1 in

Additional Product Features

Edition Number
2
Dewey Edition
23
Number of Volumes
1 Vol.
Illustrated
Yes
Dewey Decimal
519.5
Lc Classification Number
Qa276-280
Table of Content
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.
Copyright Date
2021