Difference between revisions of "10-601 Logistic Regression"
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=== Assignment === | === Assignment === | ||
| − | * Implement logistic regression and apply it to a couple of datasets, using | + | * Implement logistic regression and apply it to a couple of datasets, using a gradient descent to optimize. Experiment by changing the regularization parameter. (Details to be posted later.) |
=== What You Should Know Afterward === | === What You Should Know Afterward === | ||
Revision as of 10:13, 16 September 2013
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 a gradient descent to optimize. 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 and different.
- What "overfitting" is, and why optimizing performance on a training set does not necessarily lead to good performance on a test set.
