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20 CS 821: Pattern Recognition
Syllabus:
Learning Objectives:
| Course Number | 20 CS 821 |
| Credit Hours | 3 Graduate |
| Prerequisites |
20 CS 634 |
| Catalog Data | Capability of classifying data points into groups with shared behavior patterns. Focus of this course is on introducing the fundamentals of various methodologies. |
| Textbooks |
Theodoridis and Koutroumbas, Pattern Recognition, Academic Press. |
| References | None |
| Prerequisites by Topic | 1. 20-CS-633, 634, Artificial Intelligence I, II 2. Basic familiarity with random variables 3. Probability Theory |
| Topics | 1. Medical, social, scientific and military applications of pattern recognition. 2. Representing information. Transformation of representations. 3. Elementary properties of estimators. Maximum likelihood estimation, Bayes estimation, Stochastic approximation. 4. Decision rules for use in pattern recognition. Minimization of risk due to decision rules. 5. Decision rules using local density estimation. Nearest-Neighbor Decision rules. 6. Dimensionality reduction, feature selection, and feature extraction. 7. Clustering algorithms. |
| Computer Usage | None |
| Labs | None |
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