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 Informationinfo.pdf
schedule.pdfCourse Schedule
AI-classes.pdfOther AI Courses

Review Notes

Linear Algebra Review and Referencecs229-linalg.pdf
Probability Theory Reviewcs229-prob.pdf
Matlab Reviewlogistic_grad_ascent.txt
sigmoid.txt
matlab_session.txt
Convex Optimization Overviewcs229-cvxopt.pdf
cs229-cvxopt2.pdf
Hidden Markov Modelscs229-hmm.pdf
Gaussian Processescs229-gp.pdf
cs229-hmm.pdf
cs229-hmm.pdf
cs229-hmm.pdf