| Week of |
Subject |
Homework assignments |
| 1 |
Jan 29 |
Overview, how to design a learning system |
|
| 2 |
Feb 5 |
Bayesian decision theory |
|
| 3 |
Feb 12 |
Bayesian decision theory |
|
| 4 |
Feb 19 |
Parametric methods: density estimation, regression |
|
| 5 |
Feb 26 |
Parametric methods: density estimation, regression |
HW1 out (Feb 28) |
| 6 |
Mar 5 |
Nonparametric methods: density estimation |
|
| 7 |
Mar 12 |
Decision trees |
HW1 in (Mar 12)
HW2 out (Mar 14) |
| 8 |
Mar 19 |
Linear discrimination |
|
| 9 |
Mar 26 |
Linear discrimination
Multilayer perceptrons |
HW2 in (Mar 26) |
| 10 |
Apr 2 |
Multilayer perceptrons |
HW3 out (Apr 4) |
| |
Apr 9 |
Spring Break |
|
| 11 |
Apr 16 |
Midterm
Unsupervised learning and clustering |
Midterm (Apr 16)
HW3 in (Apr 20) |
| 12 |
Apr 23 |
Unsupervised learning and clustering |
No class (Apr 23) |
| 13 |
Apr 30 |
Hidden Markov models |
HW4 out (May 2) |
| 14 |
May 7 |
Genetic algorithms |
|
| 15 |
May 14 |
Reinforcement learning |
HW4 in (May 14) |