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] [http://www.cs.cmu.edu/~wcohen/10-601/logreg.pdf in PDF] | |
| − | * [http://www.cs.cmu.edu/~wcohen/10-601/logreg.pptx | ||
=== Readings === | === Readings === | ||
| − | * [http://www.cs.cmu.edu/~wcohen/10-605/notes/sgd-notes.pdf William's notes on SGD | + | * Optional: |
| − | * [http://cseweb.ucsd.edu/~elkan/250B/logreg.pdf Charles Elkan's notes on SGD] | + | ** Murphy 8.1-3, 8.6 |
| − | + | ** [http://www.cs.cmu.edu/~wcohen/10-605/notes/sgd-notes.pdf William's notes on SGD] sec 1-3 | |
| − | + | ** [http://cseweb.ucsd.edu/~elkan/250B/logreg.pdf Charles Elkan's notes on SGD] | |
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=== What You Should Know Afterward === | === What You Should Know Afterward === | ||
* How to implement logistic regression. | * How to implement logistic regression. | ||
| − | * Why regularization matters. | + | * 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. | * 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. | * What "overfitting" is, and why optimizing performance on a training set does not ''necessarily'' lead to good performance on a test set. | ||
Latest revision as of 01:37, 18 February 2016
This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016
Slides
- William's lecture: in Powerpoint in PDF
Readings
- Optional:
- Murphy 8.1-3, 8.6
- William's notes on SGD sec 1-3
- 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.
