Course Materials
CS229 - Machine Learning (translation in progress)
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: Matlab Tutorial and A Practical Introduction to Matlab. 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 Octave Tutorial and Octave on Wiki
Viewing PostScript and PDF files
Depending on the computer you are using, you may be able to download a PostScript viewer or PDF viewer
Course Handouts
| Course Information | info.pdf |
| schedule.pdf | Course Schedule |
| AI-classes.pdf | Other AI Courses |
Review Notes
| Linear Algebra Review and Reference | cs229-linalg.pdf |
| Probability Theory Review | cs229-prob.pdf |
| Matlab Review | logistic_grad_ascent.txt |
| sigmoid.txt | |
| matlab_session.txt | |
| Convex Optimization Overview | cs229-cvxopt.pdf |
| cs229-cvxopt2.pdf | |
| Hidden Markov Models | cs229-hmm.pdf |
| Gaussian Processes | cs229-gp.pdf |
| cs229-hmm.pdf | |
| cs229-hmm.pdf | |
| cs229-hmm.pdf |