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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.

Prerequisites

(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

Not Reported

Offered

Not Reported

Grade Point Average
3.01

-12.09% from Historical

Completion Rate
94.55%

-3.65% from Historical

A Rate
20%

-57.42% from Historical

Class Size
110

-11.92% from Historical

Cumulative Grade Distribution

Instructors (2026 Summr)

Sorted by ratings from Rate My Professors

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