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An Introduction to Statistical Learning
Overview of attention for book
Table of Contents
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Book Overview
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Chapter 1
Introduction
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Chapter 2
Statistical Learning
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Chapter 3
Linear Regression
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Chapter 4
Classification
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Chapter 5
Resampling Methods
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Chapter 6
Linear Model Selection and Regularization
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Chapter 7
Moving Beyond Linearity
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Chapter 8
Tree-Based Methods
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Chapter 9
Support Vector Machines
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Chapter 10
Unsupervised Learning
Overall attention for this book and its chapters
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Mentioned by
news
3
news outlets
blogs
7
blogs
policy
4
policy sources
twitter
196
X users
syllabi
3
institutions with syllabi
facebook
4
Facebook pages
wikipedia
12
Wikipedia pages
googleplus
1
Google+ user
Citations
dimensions_citation
8638
Dimensions
Readers on
mendeley
9618
Mendeley
citeulike
7
CiteULike
Book overview
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. Unsupervised Learning
Summary
News
Blogs
Policy documents
X
Syllabi
Facebook
Wikipedia
Google+
Dimensions citations
This data is correct as of December 2015 - for more up to date information, please visit
https://opensyllabus.org/
So far, Altmetric has seen this research output assigned in
6
syllabi from
3
institutions on Open Syllabus Project.
Institution
Syllabi count
Course subject areas covered
George Mason University
4
Business
San Jose State University
1
Engineering
Unknown
1
Unknown