Difference between revisions of "10-601 Linear Regression"
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** Bishop 3.1 | ** Bishop 3.1 | ||
| − | * A less technical [https://www.youtube.com/watch?v=DQWI1kvmwRg video lecture] on overfitting and bias-variance | + | * Optional: |
| + | ** A nice but somewhat less technical [https://www.youtube.com/watch?v=DQWI1kvmwRg video lecture] on overfitting and bias-variance | ||
=== What You Should Know Afterward === | === What You Should Know Afterward === | ||
Revision as of 10:09, 27 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.
- Side note: The bias-variance decomposition.
Readings
- Mitchell 4.1-4.3
- Murphy: 7.1-7.3, 7.5.1
- Optional:
- Bishop 3.1
- Optional:
- A nice but somewhat less technical video lecture on overfitting and bias-variance
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
