| Week of |
Subject |
Homework assignments |
| 1 |
Jan 30 |
Overview, how to design a learning system |
|
| 2 |
Feb 6 |
Bayesian decision theory |
|
| 3 |
Feb 13 |
Parametric methods: density estimation, regression, multivariate data |
|
| 4 |
Feb 20 |
Parametric methods: density estimation, regression, multivariate data |
HW1 out (Feb 23) |
| 5 |
Feb 27 |
Nonparametric methods: density estimation |
HW1 in (Mar 2)
HW2 out (Mar 2) |
| 6 |
Mar 6 |
Decision trees |
HW2 in (Mar 9) |
| 7 |
Mar 13 |
Linear discrimination |
|
| 8 |
Mar 20 |
Linear discrimination, review |
Midterm (tentative
date: Mar 23) |
| |
Mar 27 |
Spring Break |
|
| 9 |
Apr 3 |
Multilayer perceptrons |
|
| 10 |
Apr 10 |
Multilayer perceptrons |
HW3 out (Apr 10) |
| 11 |
Apr 17 |
Unsupervised learning and clustering |
HW3 in (Apr 20) |
| 12 |
Apr 24 |
Hidden Markov models |
HW4 out (Apr 27) |
| 13 |
May 1 |
Reinforcement learning |
HW4 in (May 4) |
| 14 |
May 8 |
Reinforcement learning, review |
HW5 out (May 8) |
| |
|
|
HW5 in (May 15) |