STAT 542
STAT 542 - Statistical Learning
Spring 2024
| Title | Rubric | Section | CRN | Type | Hours | Times | Days | Location | Instructor |
|---|---|---|---|---|---|---|---|---|---|
| Statistical Learning | ASRM551 | A1 | 68736 | LCD | 4 | 1500 - 1550 | M W F | 1002 Lincoln Hall | Jingbo Liu |
| Statistical Learning | CSE542 | A1 | 63828 | LCD | 4 | 1500 - 1550 | M W F | 1002 Lincoln Hall | Jingbo Liu |
| Statistical Learning | STAT542 | A1 | 61880 | LCD | 4 | 1500 - 1550 | M W F | 1002 Lincoln Hall | Jingbo Liu |
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Official Description
Modern techniques of predictive modeling, classification, and clustering are discussed. Examples of these are linear regression, nonparametric regression, kernel methods, regularization, cluster analysis, classification trees, neural networks, boosting, discrimination, support vector machines, and model selection. Applications are discussed as well as computation and theory. Course Information: Same as ASRM 551 and CSE 542. 4 graduate hours. No professional credit. Prerequisite: STAT 410 and STAT 425.