CS 229
Machine Learning
Course Materials

Handouts and Problem Sets

Lecture Notes

Supplemental Notes

Section Notes

Other resources

Advice on applying machine learning: Slides from Andrew's lecture on getting machine learning algorithms to work in practice can be found here.

Previous projects: A list of last year's final projects can be found here.

Matlab resources: Here are a couple of Matlab tutorials that you might find helpful: and For emacs users only: If you plan to run Matlab in emacs, here are matlab.el, and a helpful .emac's file.

Octave resources: For a free alternative to Matlab, check out GNU Octave. The official documentation is available here. Some useful tutorials on Octave include and .

Data: Here is the UCI Machine learning repository, which contains a large collection of standard datasets for testing learning algorithms. If you want to see examples of recent work in machine learning, start by taking a look at the conferences NIPS (all old NIPS papers are online) and ICML. Some other related conferences include UAI, AAAI, IJCAI.

Viewing PostScript and PDF files: Depending on the computer you are using, you may be able to download a PostScript viewer or PDF viewer for it if you don't already have one.

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