10-601 Linear Regression
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Revision as of 09:45, 16 September 2014 by Wcohen (talk | contribs) (→What You Should Know Afterward)
This a lecture used in the Syllabus for Machine Learning 10-601 in Fall 2014
Slides
- Ziv's lecture: Slides in pdf.
- William's lecture: Slides in Powerpoint.
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
