Difference between revisions of "10-601 Logistic Regression"
From Cohen Courses
Jump to navigationJump to search (→Slides) |
(→Slides) |
||
| Line 2: | Line 2: | ||
=== 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] [http://www.cs.cmu.edu/~wcohen/10-601/logreg.pdf in PDF] |
=== Readings === | === Readings === | ||
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.
