Difference between revisions of "10-601 Logistic 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 === | ||
| − | + | * William's lecture: [http://www.cs.cmu.edu/~wcohen/10-601/logreg.pptx in Powerpoint] | |
| − | * William's lecture: [http://www.cs.cmu.edu/~wcohen/10-601/logreg.pptx in Powerpoint] | ||
| − | |||
=== Readings === | === Readings === | ||
* Optional: | * Optional: | ||
| − | ** | + | ** Murphy 8.1-3, 8.6 |
** [http://www.cs.cmu.edu/~wcohen/10-605/notes/sgd-notes.pdf William's notes on SGD (for 10605)] | ** [http://www.cs.cmu.edu/~wcohen/10-605/notes/sgd-notes.pdf William's notes on SGD (for 10605)] | ||
** [http://cseweb.ucsd.edu/~elkan/250B/logreg.pdf Charles Elkan's notes on SGD] | ** [http://cseweb.ucsd.edu/~elkan/250B/logreg.pdf Charles Elkan's notes on SGD] | ||
| − | |||
=== What You Should Know Afterward === | === What You Should Know Afterward === | ||
Revision as of 15:14, 6 January 2016
This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016
Slides
- William's lecture: in Powerpoint
Readings
- Optional:
- Murphy 8.1-3, 8.6
- William's notes on SGD (for 10605)
- Charles Elkan's notes on SGD
What You Should Know Afterward
- How to implement logistic regression.
- How to determine the best parameters for logistic regression models
- Why regularization matters for logistic regression.
- How logistic regression and naive Bayes are similar and different.
- The difference between a discriminative and a generative classifier.
- What "overfitting" is, and why optimizing performance on a training set does not necessarily lead to good performance on a test set.
