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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.

Prerequisites

(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

Not Reported

Offered

Not Reported

Grade Point Average
3.44

0.81% from Historical

Completion Rate
97.87%

1.37% from Historical

A Rate
44.68%

-15.6% from Historical

Class Size
47

-4.67% from Historical

Cumulative Grade Distribution

Instructors (2026 Summr)

Sorted by ratings from Rate My Professors

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