Difference between revisions of "10-601 Logistic Regression"
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=== Slides === | === Slides === | ||
* Ziv's lecture: [http://www.cs.cmu.edu/~zivbj/classF14/LR.pdf Slides in pdf]. | * Ziv's lecture: [http://www.cs.cmu.edu/~zivbj/classF14/LR.pdf Slides in pdf]. | ||
| − | * [http://www.cs.cmu.edu/~wcohen/10-601/logreg.pptx | + | * 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]. |
=== Readings === | === Readings === | ||
Revision as of 13:40, 17 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: in Powerpoint, in PDF.
Readings
- Optional:
- Bishop 4.2-4.3
- William's notes on SGD (for 10605)
- Charles Elkan's notes on SGD
- Lazy Sparse Stochastic Gradient Descent for Regularized Multinomial Logistic Regression, Carpenter, Bob. 2008. See also his blog post on logistic regression.
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.
