An introduction to statistical learning : with applications in R

By: James GarethContributor(s): Daniela Witten | Trevor Hastie | Robert TibshiraniMaterial type: TextTextSeries: Springer Texts in StatisticsPublication details: New York: Springer, [c2013]Description: 426 pISBN: 9781461471370Subject(s): StatisticsLOC classification: QA 276
Contents:
Ch 1. Introduction Ch 2. Statistical Learning Ch 3. Linear Regression Ch 4. Classification Ch 5. Resampling Methods Ch 6. Linear Model Selection and Regularization Ch 7. Moving Beyond Linearity Ch 8. Tree-Based Methods Ch 9. Support Vector Machines Ch 10. Unsupervised Learning
Summary: 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. --- summary provided by publisher
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Item type Current library Collection Shelving location Call number Status Notes Date due Barcode Item holds
Book Book ICTS
Mathematic Rack No 5 QA 276 (Browse shelf (Opens below)) Available Invoice no. IN 1184 ; Date 06-12-2019 02259
Total holds: 0

Ch 1. Introduction
Ch 2. Statistical Learning
Ch 3. Linear Regression
Ch 4. Classification
Ch 5. Resampling Methods
Ch 6. Linear Model Selection and Regularization
Ch 7. Moving Beyond Linearity
Ch 8. Tree-Based Methods
Ch 9. Support Vector Machines
Ch 10. Unsupervised Learning

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. --- summary provided by publisher

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