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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
8
Resampling
Start:
Monday, March 15
End:
Friday, March 19
8.1
Summary
Coming soon!
8.2
Learning Objectives
Coming soon!
8.3
Reading
Coming soon!
8.4
Video
Coming soon!
8.5
Assignments
Coming soon!
8.6
Office Hours
Coming soon!
8.7
Additional Information
Coming soon!
7
Generative Models
9
Regularization
On this page
8
Resampling
8.1
Summary
8.2
Learning Objectives
8.3
Reading
8.4
Video
8.5
Assignments
8.6
Office Hours
8.7
Additional Information