| Week | Topic |
| 1 | Basics in supervised learning |
| 2-5 |
Linear methods for classification (Project 1)
a. Linear regression b. LDA c. Logistic regression |
| 6-7 |
Prototype methods for classification
a. K-means b. Learning vector quantization c. K nearest-neighbor |
| 8 |
Clustering methods
a. K-center b. Pairwise distance based clustering: dendrogram (Project 2) |
| 9-11 | Classification and Regression Trees (Project 3) |
| 11-15 | Mixture discriminant analysis (Project 4) |
| 15-16 | Hidden Markov model and its applications |