CS 5973 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 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)

Project Resources

• Ray Mooney's hints on how to write a ML project final report
• My list of suggested projects
• Ray Mooney's list of suggested projects (for ML class at UT)
• Machine Learning datasets available at the ML repository at UCI
• How to design an experiment:
  • Rob Holte's slides on experiment design
  • Tom Dietterich and Marie desJardins' slides on experiment design
• Places to find related papers (that may give you ideas on projects):
  • 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, ... 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)