# 10-601 Logistic Regression

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Jump to navigationJump to searchThis 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.