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Summer 2021
Oct 22, 2021
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Information Select the desired Level or Schedule Type to find available classes for the course.

MATH 50028 - STATISTICAL LEARNING
(Slashed with MATH 40028)This course is about the statistical foundations of modern machine learning techniques. The main focus is classification and prediction, using regression-based, tree-based, and kernel-based methods. Specific methods include logistic regression, classification and regression trees, random forests, and support vector machines. The course also includes an introduction to unsupervised and semi-supervised learning. Prerequisite: MATH 40015 or 50015 and MATH 40024 or 50024; and applied mathematics or pure mathematics major; and graduate standing.
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
Must be enrolled in one of the following Majors:     
      Applied Mathematics
      Pure Mathematics

Prerequisites:
Prereq for MATH 50028

General Requirements:
Course or Test: MATH 40015
Minimum Grade of C
May not be taken concurrently.  )
or
Course or Test: MATH 50015
Minimum Grade of C
May not be taken concurrently.  )
and
Course or Test: MATH 40024
Minimum Grade of C
May not be taken concurrently.  )
or
Course or Test: MATH 50024
Minimum Grade of C
May not be taken concurrently. )


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