Difference between revisions of "10-601 Linear Regression"
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| − | This a lecture used in the [[Syllabus for Machine Learning 10- | + | This a lecture used in the [[Syllabus for Machine Learning 10-601B in Spring 2016]] |
=== Slides === | === Slides === | ||
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* William's lecture: [http://www.cs.cmu.edu/~wcohen/10-601/linear-regression.ppt Slides in Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-601/linear-regression.pdf in PDF]. | * William's lecture: [http://www.cs.cmu.edu/~wcohen/10-601/linear-regression.ppt Slides in Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-601/linear-regression.pdf in PDF]. | ||
Revision as of 16:36, 6 January 2016
This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016
Slides
- William's lecture: Slides in Powerpoint, in PDF.
Readings
- Mitchell 4.1-4.3
- Optional:
- Bishop 3.1
What You Should Know Afterward
- Regression vs. classification
- Solving regression problems with 1 and 2 variables
- Ordinary least squares (OLS) solution (aka normal equations) to linear regression problems
- Gradient descent approach to linear regression
- Data transformation and its impact on the way linear regression is solved, and the expressiveness of LR models
