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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
Ensemble Methods
12
Analysis I
13
Analysis II
14
Analysis III
15
Unsupervised Learning
16
The End
Resources
Acknowledgments
7
Generative Models
Start:
Monday, March 8
End:
Friday, March 12
7.1
Summary
Coming soon!
7.2
Learning Objectives
Coming soon!
7.3
Reading
Coming soon!
7.4
Video
Coming soon!
7.5
Assignments
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7.6
Office Hours
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7.7
Additional Information
Coming soon!
6
Binary Classification
8
Resampling
On this page
7
Generative Models
7.1
Summary
7.2
Learning Objectives
7.3
Reading
7.4
Video
7.5
Assignments
7.6
Office Hours
7.7
Additional Information