Detailed Course Information |
Spring 2025
Feb 05, 2025 |
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| MATH 624 - Introduction to Statistical Learning |
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Supervised learning: classification, linear discriminant analysis, quadratic discriminant analysis, multiple discriminant analysis, model selection regularization, bootstrap methods. Unsupervised learning: principal component analysis, canonical correlation, clustering methods. Prerequisite: MATH 620 or DSCI 602 or permission of the department chairperson.
3.000 Credit hours 3.000 Lecture hours Levels: Graduate Schedule Types: Lecture, Study Abroad, Online Fixed Times (Synch), Online (Asynchronous) Mathematical Sciences Department Restrictions: Must be enrolled in one of the following Levels: Graduate Prerequisites: Graduate level MATH 620 Minimum Grade of D- or Graduate level DSCI 602 Minimum Grade of D- |
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