| Line 7: | Line 7: | ||
<br> | <br> | ||
| − | {| width="55%" | + | {| width="55%" cellspacing="1" cellpadding="1" border="1" |
|- | |- | ||
| − | ! scope="col" | Lecture | + | ! scope="col" | Lecture |
! scope="col" | Topic | ! scope="col" | Topic | ||
|- | |- | ||
| − | | 1 | + | | 1 |
| 1. Introduction | | 1. Introduction | ||
|- | |- | ||
| − | | 1 | + | | 1 |
| 2. What is pattern Recognition | | 2. What is pattern Recognition | ||
|- | |- | ||
| − | | 2-3 | + | | 2-3 |
| 3. Finite vs Infinite feature spaces | | 3. Finite vs Infinite feature spaces | ||
|- | |- | ||
| − | | 4-5 | + | | 4-5 |
| 4. Bayes Rule | | 4. Bayes Rule | ||
|- | |- | ||
| − | | 6-10 | + | | 6-10 |
| | | | ||
| − | 5. | + | 5. Discriminant functions |
| − | + | *Definition; | |
| − | + | *Application to normally distributed features; | |
| − | + | *Error analysis. | |
| − | + | ||
| − | + | ||
|- | |- | ||
| − | | 11-12 | + | | 11-12 |
| | | | ||
| − | 6. Parametric Density Estimation | + | 6. Parametric Density Estimation |
| − | + | *Maximum likelihood estimation | |
| − | + | *Bayesian parameter estimation | |
| − | + | ||
|- | |- | ||
| | | | ||
| | | | ||
| − | 7. Non-parametric Density Estimation | + | 7. Non-parametric Density Estimation |
| − | + | *Parzen Windows | |
| − | + | *K-nearest neighbors | |
| − | + | *The nearest neighbor classification rule. | |
| − | + | ||
| − | + | ||
|- | |- | ||
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|- | |- | ||
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| − | |||
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| − | + | 9. More on Non-Linear Discriminant functions | |
| − | + | ||
| − | + | *Support Vector Machines | |
| − | + | *Artificial Neural Networks | |
| + | *Decision Trees | ||
| + | |||
|- | |- | ||
| | | | ||
| − | | | + | | 10. Clustering |
|} | |} | ||
Revision as of 09:39, 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. Discriminant functions
|
| 11-12 |
6. Parametric Density Estimation
|
|
7. Non-parametric Density Estimation
| |
| 8. Linear Discriminants | |
|
9. More on Non-Linear Discriminant functions
| |
| 10. Clustering |
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