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STAT 432
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Table of contents
Basics of Statistical Learning
Syllabus
1
Overview
2
Linear Regression
3
Nonparametric Regression
4
Classification Introduction
5
Exam I
6
Binary Classification
7
Generative Models
8
Resampling
9
Regularization
10
Exam II
11
Break
12
Ensemble Methods
13
Analysis I
14
Analysis II
15
Unsupervised Learning
16
The End
Resources
Acknowledgments
16
The End
Start:
Monday, May 10
End:
Friday, May 14
16.1
Summary
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16.2
Learning Objectives
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16.3
Reading
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16.4
Video
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16.5
Assignments
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16.6
Office Hours
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16.7
Additional Information
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15
Unsupervised Learning
Resources
On this page
16
The End
16.1
Summary
16.2
Learning Objectives
16.3
Reading
16.4
Video
16.5
Assignments
16.6
Office Hours
16.7
Additional Information