Difference between revisions of "10-601 Linear Regression"
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* Regression vs. classification | * Regression vs. classification | ||
* Solving regression problems with 1 and 2 variables | * Solving regression problems with 1 and 2 variables | ||
| − | * | + | * Ordinary least squares (OLS) solution (aka normal equations) to linear regression problems |
| − | * Data transformation and its impact on the way | + | * Gradient descent approach to linear regression |
| + | * Data transformation and its impact on the way linear regression is solved, and the expressiveness of LR models | ||
Revision as of 10:45, 16 September 2014
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
