CS 229 Machine Learning Course Materials

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Handouts and Problem Sets

Handout #1: Course Information (HTML) Handout #2: Course Schedule (HTML) Handout #3: Cover Sheet Problem Set 1 (pdf) Data: q1x.dat, q1y.dat, q2x.dat, q2y.dat PS1 Solution (pdf) Handout #4: Final Project Guidelines (PDF) Problem Set 2 (pdf), PS2 Solution (pdf) Practice Midterm (pdf) Solutions Problem Set 3 (pdf) Data: mandrill-small.tiff, mandrill-large.tiff PS3 Solution (pdf)
• Problem Set 4 (pdf) PS4 Solution (pdf)
  

Lecture Notes

Lecture notes 1 (ps) (pdf)   Supervised Learning, Discriminative Algorithms Lecture notes 2 (ps) (pdf)   Generative Algorithms Lecture notes 3 (ps) (pdf)   Support Vector Machines Lecture notes 4 (ps) (pdf)   Learning Theory Lecture notes 5 (ps) (pdf)   Regularization and Model Selection Lecture notes 6 (ps) (pdf)   Online Learning and the Perceptron Algorithm. (optional reading) Lecture notes 7a (ps) (pdf)   Unsupervised Learning, k-means clustering. Lecture notes 7b (ps) (pdf)   Mixture of Gaussians Lecture notes 8 (ps) (pdf)   The EM Algorithm Lecture notes 9 (ps) (pdf)   Factor Analysis Lecture notes 10 (ps) (pdf)   Principal Components Analysis Lecture notes 11 (ps) (pdf)   Independent Components Analysis
• Lecture notes 12 (ps) (pdf)   Reinforcement Learning and Control
  

Section Notes

Section notes 1 (pdf)   Linear Algebra Review and Reference Section notes 2 (pdf)   Probability Theory Review Files for the Matlab tutorial: sigmoid.m, logistic_grad_ascent.m, matlab_session.m Section notes 4 (ps) (pdf)   Convex Optimization Overview, Part I Section notes 5 (ps) (pdf)   Convex Optimization Overview, Part II Section notes 6 (ps) (pdf)   Hidden Markov Models Section notes 7 (pdf)   The Multivariate Gaussian Distribution Section notes 8 (pdf)   More on Gaussian Distribution
• Section notes 9 (pdf)   Gaussian Processes
  

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: http://www.math.ufl.edu/help/matlab-tutorial/ and http://www.math.mtu.edu/~msgocken/intro/node1.html. 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 http://en.wikibooks.org/wiki/Octave_Programming_Tutorial and http://www-mdp.eng.cam.ac.uk/web/CD/engapps/octave/octavetut.pdf .
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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