Lectures notes will be available here as gzipped postscripts and source (latex) files.
The style file for scribing the lectures is here.
Most notes are based on a draft from the 97 lectures. Ask for the draft before you start working on your lecture notes.

1. Lecture #1:
            Introduction to Computational Learning;The PAC model (8/31,9/2) tex
2. Lecture #2:
            The importance of representation: Learning 2-term DNF is hard. (9/7,9/9) tex
3. Lecture #3:
            Occam's Razor: VC dimension. (9/16,9/18) tex
4. Presentation #1:
            Margin Distribution Bounds on Generalization (Presented by Ming-Hsuan Yang) tex
(9/21,9/23)
5. Lecture #4:
            On Line Learning(9/28,9/30) tex
6. Presentation #2:
            Projection Learning (Presented by Shripad Thite)(10/7) tex
7. Presentation #3:
            The Robustness of p-norm algorithms (Presented by Shaojun Wang)(10/12,10/14)
8. Presentation #4:
            Drifting Games (Presented by Ting-Hao Yang)(10/19)
9. Lecture #5:
            Active Learning(10/21,10/26) tex
10. Presentation #6
             : Learning Automata (Presented by Vasin Punyakanok)(10/28)
11. Presentation #5:
             Combining labeled and unlabeled data with Co-Training (Presented by Dav Zimak)(11/2)
12. Lecture #6:
             Reasoning with Models(11/9,11/4) tex
13. Lecture #7:
             Learning to Reason(11/16,11/11) tex
14. Presentation #7:
             Learning to take Action (Presented by Oleg Pashko) (11/11)
15. Lecture #8:
             Introduction to Probabilistic Reasoning(11/18) tex
16. Presentation #8:
             Query DAGS: A paradigm for implementing Belief Network Inference (Presented by Sun Qiang ) (11/18)
17. Presentation #9:
             Correction of belief propagation in Gaussian graphical models (Presented by Cedric Yau) (11/23)