| Week of |
Subject |
Homework assignments |
| 1 |
Feb 9 |
Overview, how to design a learning system |
|
| 2 |
Feb 16 |
Bayesian decision theory |
|
| 3 |
Feb 23 |
Parametric methods |
|
| 4 |
Mar 2 |
Parametric methods, cross-validation, dimensionality reduction |
HW1 out (Mar 6) |
| 5 |
Mar 9 |
Nonparametric methods |
|
| 6 |
Mar 16 |
Decision trees |
HW1 in (Mar 16)
HW2 out (Mar 20) |
| 7 |
Mar 23 |
Linear discrimination |
|
| 8 |
Mar 30 |
Multilayer perceptrons |
HW2 in (Mar 30)
HW3 out (Apr 3) |
| 9 |
Apr 6 |
Unsupervised learning and clustering |
|
| 10 |
Apr 13 |
Hidden Markov models, reinforcement learning |
HW3 in (Apr 13)
HW4 out (Apr 17) |
| 11 |
Apr 20 |
Midterm |
No class (Apr 23) |
| 12 |
Apr 27 |
Applications of machine learning
(student presentations) |
HW4 in (Apr 27)
HW5 out (May 1) |
| 13 |
May 4 |
Applications of machine learning
(student presentations) |
|
| 14 |
May 11 |
Applications of machine learning
(student presentations) |
HW5 in (May 11) |