| Week of |
Subject |
Homework assignments |
| 1 |
Feb 11 |
Overview, how to design a learning system |
|
| 2 |
Feb 18 |
Bayesian decision theory |
|
| 3 |
Feb 25 |
Bayesian decision theory |
|
| 4 |
Mar 3 |
Parametric methods: density estimation, regression |
|
| 5 |
Mar 10 |
Parametric methods: density estimation, regression |
HW1 out (Mar 12) |
| 6 |
Mar 17 |
Nonparametric methods: density estimation |
|
| 7 |
Mar 24 |
Decision trees |
HW1 in (Mar 24) |
| 8 |
Mar 31 |
Midterm |
HW2 out (Mar 31) |
| 9 |
Apr 7 |
Linear discrimination |
|
| 10 |
Apr 14 |
Multilayer perceptrons |
HW2 in (Apr 14) |
| 11 |
Apr 21 |
Multilayer perceptrons |
HW3 out (Apr 21)
No class (Apr 23) |
| 12 |
Apr 28 |
Unsupervised learning and clustering |
|
| 13 |
May 5 |
Hidden Markov models |
HW3 in (May 5)
HW4 out (May 7) |
| 14 |
May 12 |
Reinforcement learning |
|
| |
|
|
HW4 in (May 20) |