Num. | Topic | Date | Source exercises |
1 | Introduction on Computational Learning | 30.10.00 | [KV] Chapter 1 |
2 | Definition of PAC model | 6.11.00 | [KV] Chapter 1 |
3 | Occam's Razor Algorithms. | 13.11.00 | [KV] Chapter 2 |
4 | Weak and Strong Learning: Adaboost. | 20.11.00 | [FS] |
5 | Boosting System for Text Categorization. | 27.11.00 | [SS] |
6-7 | Learning with Classification Noise. | 4.12.00, 11.12.00 | [KV] Chapter 5, [Kearns] |
8 | Noise-tolerant Learning and the Parity problem. | 18.12.00 | [BKW] |
9 | Learning in the Presence of Malicious Noise. | 25.12.00 | [KL] |
10 | Learning in the On-line Model. | 1.1.01 |   |
11 | The Winnow Algorithm. | 8.1.01 | [Littlestone] |
12 | Subexponential learning of DNF. | 15.1.01 | [Bshouty] |
13 | Seminar Summary. | 22.1.01 |   |