Course Outline, ECE662 Spring 2010 Prof. Mimi
Note: This is an approximate outline that is subject to change throughout the semester.
| Lecture | Topic |
|---|---|
| 1 | 1. Introduction |
| 1 | 2. What is pattern Recognition |
| 2-3 | 3. Finite vs Infinite feature spaces |
| 4-5 | 4. Bayes Rule |
| 6-10 |
5. Discriminate functions - Definition; - Application to normally distributed features; - Error analysis. |
| 11-12 |
6. Parametric Density Estimation -Maximum likelihood estimation -Bayesian parameter estimation |
|
7. Non-parametric Density Estimation -Parzen Windows -K-nearest neighbors -The nearest neighbor classification rule. | |
| 8. Linear Discriminants | |
| 9. SVM | |
| 10. ANN | |
| 11. Decision Trees | |
| 12. Clustering |
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