Detailed Course Information |
Fall 2024
Nov 21, 2024 |
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| MATH 50028 - STATISTICAL LEARNING |
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(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 MATH 50015; and MATH 40024 or MATH 50024; and Applied Mathematics major or Data Science major 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 Data Science 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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