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Fall 2017
Oct 18, 2017
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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 permission of the department chairperson.
3.000 Credit hours
3.000 Lecture hours

Levels: Graduate
Schedule Types: Lecture

Mathematical Sciences Department

Restrictions:
Must be enrolled in one of the following Levels:     
      Graduate

Prerequisites:
Graduate level MATH 620 Minimum Grade of D-

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