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MATRIX METHODS IN MACHINE LEARNING

COMPSCI/ECE/ME 532
Course Description

Linear algebraic foundations of machine learning featuring real-world applications of matrix methods from classification and clustering to denoising and data analysis. Mathematical topics include: linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. Machine learning topics include: the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. Previous exposure to numerical computing (e.g. Matlab, Python, Julia, R) required.

Prerequisties

(MATH 234 , MATH 320 , MATH 340 , MATH 341 , or MATH 375 ) and (ECE 203 , COMPSCI 200 , COMPSCI 220 , COMPSCI 300 , 301, 302, COMPSCI 310 , COMPSCI 320 , or placement into COMPSCI 300 ), graduate/professional standing, or declared in Capstone Certificate in Computer Sciences for Professionals

Satisfies
Credits

3

Offered

Spring

Grade Point Average
3.4

-1.15% from Historical

Completion Rate
97.37%

-0.96% from Historical

A Rate
46.05%

-4.78% from Historical

Class Size
152

20.86% from Historical

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

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