10-601 Logistic Regression
From Cohen Courses
This a lecture used in the Syllabus for Machine Learning 10-601 in Fall 2014
Slides
Readings
- 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.
- 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.
