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MATHEMATICAL METHODS IN DATA SCIENCE

MATH 535
Course Description

A rigorous introduction to mathematical concepts important for modern data science. Topics include: matrix factorizations, optimization theory and algorithms, probabilistic models, finite Markov chains. Mathematical techniques are motivated by and illustrated on a range of applied problems from machine learning and statistics.

Prerequisties

(MATH 320 , MATH 340 , MATH 341 , MATH 375 or COMPSCI/ECE/ME 532 ) and (MATH/STAT 309 , MATH/STAT 431 , MATH 531 , STAT 311 or ECE 331 ) and (MATH 322 , MATH 341 , MATH 375 , MATH 421 , MATH 467 , or COMPSCI 577 ), graduate/professional standing, or member of Pre-Masters Mathematics (Visiting Intl) Prgrm

Satisfies

This course does not satisfy any prerequisites.

Credits

3

Offered

Not Applicable

Grade Point Average
3.46

1.64% from Historical

Completion Rate
94.87%

-1.6% from Historical

A Rate
64.1%

19.12% from Historical

Class Size
39

-21.3% from Historical

2025 Fall Grade Distribution

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

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