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Detailed Course Information


Fall 2021
Nov 29, 2021
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MATH 624 - Introduction to Statistical Learning
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, Online Fixed Times (Synch), Online (Asynchronous), Immersive Learning, Study Abroad, Societal Issue/Global Chal

Mathematical Sciences Department

Must be enrolled in one of the following Levels:     

Graduate level MATH 620 Minimum Grade of D- or Graduate level DSCI 602 Minimum Grade of D-

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