CS 229 Machine Learning Course Materials
Handouts and Problem Sets
- Handout #1: Course Information (HTML)
- Handout #2: Course Schedule (HTML)
Supplemental notes 1 (pdf) Binary classification with +/-1 labels.
Supplemental notes 2 (pdf) Boosting algorithms and weak learning.
- Matlab code to generate plots (.m) Functional after implementing stump_booster.m in PS2.
Supplemental notes 3 (pdf) The representer theorem.
Supplemental notes 4 (pdf) Hoeffding’s inequality.
Section notes 1 (pdf) Linear Algebra Review and Reference
Section notes 2 (pdf) Probability Theory Review
Section notes 7 (pdf) The Multivariate Gaussian Distribution
Section notes 8 (pdf) More on Gaussian Distribution
Section notes 9 (pdf) Gaussian Processes
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.
Here are a couple of Matlab tutorials that you might find helpful: http://www.math.ucsd.edu/~bdriver/21d-s99/matlab-primer.html 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.
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.