CS 4973 (soon 4033)/5033 Machine Learning

Course Information

Course Readings: The textbook for the course is Machine Learning by Tom Mitchell. Textbook readings will be supplemented with chapters from Reinforcement Learning: An Introduction by Sutton & Barto, Artificial Intelligence, a Modern Approach by Russell and Norvig, and by recent papers drawn from the International Conference on Machine Learning, the Journal of Machine Learning Research, and the Machine Learning Journal.
The course syllabus is available here.
Additional information, discussion boards, and announcements will be available on Desire2Learn.
Class schedule (updated as the semester progresses)

Coming up with a project:
- My list of suggested projects for 2006
- My list of suggested projects for 2005
- Ray Mooney's list of suggested projects (for his ML class at UT)
- A sample of final project reports from Fall 2005 (No, you can't repeat a project but this tells you the scope of previous projects)
Finding related research:
- Journal of Machine Learning Research (papers available for free)
- Machine Learning Journal (expensive but some articles available for free)
- International Conference on Machine learning (2003, 2004, 2005, 2006, ... google for earlier years, papers are generally available for free, try citeseer or google scholar)
- Journal of Artificial Intelligence Research (papers available for free)
- Citeseer (online paper search engine) and Google Scholar (ditto)
Acquiring data (if the source is not already specified from your project):
- Machine Learning datasets available at the ML repository at UCI
- Write your own simulator
- Use the hardware
Designing your experiments:
- Rob Holte's slides on experiment design
- Tom Dietterich and Marie desJardins' slides on experiment design
Writing it up:
- Ray Mooney's hints on how to write a ML project final report
- A sample of final project reports from Fall 2005

Last updated: August 29, 2006 2:29 PM