| Date | Lecture Topic | Reading | Graded Events |
|---|---|---|---|
| 01/11 01/13 |
Course Introduction GAs: An Overview |
Chapter 1 |
|
| 01/16 01/18 01/20 |
Martin Luther King Day! GAs in Problem Solving |
2.1 2.2, 2.3 |
|
| 01/23 01/25 01/27 |
GAs in Scientific Models Theoretical Foundations of GAs |
3.1 3.2 - 3.4 4.1, 4.2 |
Ian
Robbie |
| 01/30 02/01 02/03 |
Implementing a GA |
4.3, 4.4 5.1 - 5.3 5.4 - 5.6 |
Greg Dave |
| 02/06 02/08 02/10 |
Conclusions and Future Directions A GECCO 2004 Best Paper |
Chapter 6 Paper |
Reggie,
Peter
Marcus, Loren Jeff |
| 02/13 02/15 02/17 |
Introduction Concept Learning (General to Specific) |
Chapter 1 2.1 - 2.5 2.6 - 2.8 |
|
| 02/20 02/22 02/24 |
President's Day! Decision Tree Learning |
3.1 - 3.5 3.6 - 3.8 |
Scott Sam |
| 02/27 03/01 03/03 |
Artificial Neural Networks |
4.1 - 4.4 4.5 - 4.6 4.7 - 4.9 |
Casey
Devin GA Project (18%) |
| 03/06 03/08 03/10 |
Review Midterm Midterms Back |
|
Midterm (10%) GA Reviews Due (2%) |
| 03/13 03/15 03/17 |
Spring Break! Spring Break! Spring Break! |
|
|
| 03/20 03/22 03/24 |
Evaluating Hypotheses
(Neal) Bayesian Learning Theory |
5.1 - 5.3 5.4 - 5.7 6.1 - 6.4 |
Ian Robbie Greg |
| 03/27 03/29 03/31 |
(Neal) Computational Learning Theory |
6.5 - 6.10 6.11 - 6.13 7.1 - 7.3 |
Reggie |
| 04/03 04/05 04/07 |
Instance Based Learning Genetic Algorithms |
7.4 - 7.6 Chapter 8 Chapter 9 |
Dave Peter Marcus |
| 04/10 04/12 04/14 |
Learning Sets of Rules University Day! |
10.1 -10.4 10.5 - 10.8 |
Loren Jeff |
| 04/17 04/19 04/21 |
Analytical Learning Combining Ind. and Anly. Learning |
Chapter 11 12.1 - 12.3 12.4 - 12.7 |
Scott Sam Casey |
| 04/24 04/26 04/28 |
Reinforcement Learning
(Neal) Class Project Presentations Class Project Presentations |
Chapter 13 |
Devin Research Project (35%) |
| 05/03 | 8:00 a.m. - 9:50 a.m.. | Final (20%) |