10-601 Logistic Regression
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Revision as of 10:35, 3 July 2013 by Wcohen (talk | contribs) (Created page with 'This a lecture used in the Syllabus for Machine Learning 10-601 === Slides === * [http://www.cs.cmu.edu/~wcohen/10-601/logreg.pptx Slides in Powerpoint]. ''Based on the sl…')
This a lecture used in the Syllabus for Machine Learning 10-601
Slides
- Slides in Powerpoint. Based on the slides I used for 10-605, they might be updated.
Readings
- None
Assignment
- Implement logistic regression and apply it to a couple of datasets, using an off-the-shelf optimization routine. Experiment by changing the regularization parameter. (Details to be posted later.)
What You Should Know Afterward
- How to implement logistic regression.
- Why regularization matters.
- How logistic regression and naive Bayes are similar/different.
