| Date |
Class Period & Lecture Topic |
Reading |
Assignment |
| Jan 08 |
1. Syllabus and Introduction |
|
|
| Jan 10 |
2. Motivation and Issues |
Chapter 1 |
|
| Jan 15 |
3. Can Machines Learn? |
Chapter 2 |
|
| Jan 17 |
4. Data, Testing and Experiment Shell |
|
Written
Assignment |
| Jan 22 |
5. Decision Trees |
Chapter 3 |
|
| Jan 24 |
6. Decision Trees |
|
Data Set
Module |
| Jan 29 |
7. Decision Trees |
|
|
| Jan 31 |
8. Perceptron |
Chapter 4 |
Experiment
Shell |
| Feb 05 |
9. Perceptron/Backpropagation |
|
|
| Feb 07 |
10. Backpropagation |
|
Decision
Tree |
| Feb 12 |
11. Backpropagation |
|
|
| Feb 14 |
12. Backpropagation |
|
|
| Feb 19 |
13. No Class (Monday Instruction) |
|
|
| Feb 21 |
14. Comparing Classifiers |
Chapter 5 |
Backpropagation |
| Feb 26 |
15. Review |
|
|
| Feb 28 |
16. No Class (Midterm) |
|
|
| |
Midterm (Feb 27–29 in the testing
center) |
| Mar 04 |
17. Bayesian Learning |
Chapter 6 |
Midterm
Report |
| Mar 06 |
18. Bayesian Learning |
|
|
| Mar 11 |
19. Bayesian Learning/Grad School |
|
|
| Mar 13 |
20. Instance-based Learning |
Chapter 8 |
|
| Mar 18 |
21. Instance-based Learning |
|
Naive
Bayes |
| Mar 20 |
22. Instance-based Learning/Issues in Machine
Learning |
|
|
| Mar 25 |
23. Artificial Ethics? |
|
|
| Mar 27 |
24. Genetic Algorithms |
Chapter 9 |
Instance-based
Learning |
| Apr 01 |
25. Genetic Algorithms |
|
|
| Apr 03 |
26. Meta-learning |
Vilalta paper |
|
| Apr 08 |
27. Reinforcement Learning |
Chapter 13 |
Genetic
Algorithm |
| Apr 10 |
28. Other Topics in Machine Learning |
|
|
| Apr 15 |
29. Review |
|
Stacking |
| Apr 17 |
Reading Day |
|
|
| Apr 19 |
Final (7:00am–10:00am in
class) |
