Date Class Period & Lecture Topic Reading Assignment
Sep 03 1. Syllabus, policies, business
Sep 05 2. Introduction and overview Web search Topic Suggestions
Sep 08 3. 1-on-1 meetings
Sep 10 4. Foundation Alpaydin
Sep 12 5. Computational Learning Theory Mitchell Your Learning System
Sep 15 6. No meetings (out of town)
Sep 17 7. No meetings (out of town) Wiki Entry
Sep 19 8. No meetings (out of town)
Sep 22 9. 1-on-1 meetings Project Proposals
Sep 24 10. Support Vector Machines Hearst Begin Paper Overviews
Sep 26 11. Support Vector Machines Burges
Sep 29 12. 1-on-1 meetings
Oct 01 13. Support Vector Machines
Oct 03 14. Clustering Xu
Oct 06 15. 1-on-1 meetings
Oct 08 16. Clustering
Oct 10 17. Manifold Learning Smith, Roweis, Tenenbaum
Oct 13 18. 1-on-1 meetings
Oct 15 19. Manifold Learning Gashler
Oct 17 20. Transduction Lundell
Oct 20 21. 1-on-1 meetings
  Midterm (October 22–24 in the Testing Center)
Oct 22 22. Review
Oct 24 23. No meetings (midterm)
Oct 27 24. 1-on-1 meetings
Oct 29 25. Ensembles Freund
Oct 31 26. Ensembles End Paper Overviews
Nov 03 27. 1-on-1 meetings
Nov 05 28. Meta-Learning Pfahringer
Nov 07 29. Meta-learning Thrun
Nov 10 30. 1-on-1 meetings
Nov 12 31. Recurrent Neural Networks Elman
Nov 14 32. Recurrent Neural Networks Werbos
Nov 17 33. 1-on-1 meetings
Nov 19 34. Spiking Neural Networks Bohte
Nov 21 35. Spiking Neural Networks Goodman, Norton
Nov 24 36. 1-on-1 meetings
Nov 25 37.
Dec 01 38. 1-on-1 meetings
Dec 03 39. Creativity in Artificial Intelligent Systems Abstract
Dec 05 40. Presentations Orals Schedule
Dec 08 41. Presentations
Dec 10 42. Presentations Your Paper
Dec 12 Reading Day
Dec 19 Final 11:00–2:00pm in class)

1-on-1 Meeting Schedule (in 3324)

3:00 Heather Chan
3:10 Aaron Dennis
3:20 Jie Long
3:30 Yan Shi
3:40 Mike Smith