| Date |
Topic |
Reading |
Assignment |
| R 01/10 |
Introduction (ppt,
pdf) |
Ch1 |
|
| T 01/15 |
Bayesian Decision Theory (ppt, pdf) |
Ch2.1-2.6 |
HW 1 (Due 01/22) |
| R 01/17 |
|
Ch2.1-2.6 |
Project 1 (Due 02/04) |
| F 01/18 |
Last Day to Drop without a "W" |
| T 01/22 |
Discriminant Function (ppt, pdf) |
Ch2.1-2.6 |
|
| R 01/24 |
|
|
|
| T 01/29 |
Parametric Estimation (ppt, pdf)
Mixture Density (ppt, pdf) |
Ch3.1-3.5 |
|
| R 01/31 |
|
|
|
| T 02/05 |
Dimensionality Reduction - FLD (ppt pdf) |
Ch3.7-3.9 |
HW 2 (Due 02/12) |
| R 02/07 |
Dimensionality Reduction - PCA (ppt, pdf) |
Ch3.7-3.9 |
Project 2 (Due 02/25) |
| T 02/12 |
Performance Evaluation (ppt, pdf) |
Ch9.6.2 |
|
| R 02/14 |
Nonparametric Density Estimation - Parzen Window (ppt, pdf) |
Ch4 |
|
| T 02/19 |
Nonparametric Density Estimation - kNN (ppt, pdf) |
Ch4 |
|
| R 02/21 |
|
|
|
| T 02/26 |
Project 2 Discussion |
|
|
| R 02/28 |
Midterm Review (ppt, pdf) |
|
|
| T 03/05 |
Fusion (ppt, pdf) |
|
HW 3 (Due 03/14) |
| R 03/07 |
Homework Review |
|
|
| T 03/12 |
Test 1 |
| R 03/14 |
Unsupervised Learning (ppt, pdf) |
Ch10 |
Project 3 (Due 04/01) |
| T 03/19 |
Spring Break - No Class |
| R 03/21 |
Spring Break - No Class |
| T 03/26 |
Unsupervised Learning
Project 3 Discussion |
|
|
| R 03/28 |
Unsupervised Learning |
|
|
| T 04/02 |
Gradient Descent (pdf,
ppt)
Test 1 Discussion |
|
|
| T 04/02 |
Last Day to Drop with a "W" |
| R 04/04 |
Decision Tree (pdf, ppt) |
|
|
| T 04/09 |
Neural Network - Perceptron (pdf, ppt) |
|
HW4 (Due 04/16) |
| R 04/11 |
Neural Network - Backpropagation (pdf, ppt) |
|
Project 4 (Due 04/23)
Final Project
Milestone 1 (Due 04/16)
Milestone
2 (Due 04/25)
Presentation (Due 05/06)
Final Report (Due 05/07) |
| T 04/16 |
Review |
|
|
| R 04/18 |
Test 2 |
| T 04/23 |
BPNN
Final Project Presentation Schedule (pdf, ppt) |
|
|
| R 04/25 |
Support Vector Machine (pdf, ppt)
Syntactic Pattern Recognition
(pdf, ppt) |
|
|
| T 05/07 |
Final Project Presentation (8:00-10:00am)
(Presentation due 05/06 11:59pm; Report due 05/07 11:59pm) |
