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| − | = Course Outline, [[ECE662]] Spring 2010 [[User: | + | = Course Outline, [[ECE662]] Spring 2010 [[User:Mboutin|Prof. Mimi]] = |
| − | + | ||
| + | Note: This is an approximate outline that is subject to change throughout the semester. | ||
| + | <br> | ||
| + | {| width="55%" border="1" cellpadding="1" cellspacing="1" | ||
| + | |- | ||
| + | ! scope="col" | Lecture | ||
| + | ! scope="col" | 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 | ||
| + | |} | ||
| + | |||
| + | <br> | ||
---- | ---- | ||
| + | |||
[[2010 Spring ECE 662 mboutin|Back to 2010 Spring ECE 662 mboutin]] | [[2010 Spring ECE 662 mboutin|Back to 2010 Spring ECE 662 mboutin]] | ||
| + | |||
| + | [[Category:2010_Spring_ECE_662_mboutin]] | ||
Revision as of 06:54, 9 March 2010
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 |
Back to 2010 Spring ECE 662 mboutin
