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DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION

MATH 616
Course Description

An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.

Prerequisties

(MATH 320 , MATH 340 , MATH 341 or MATH 375 ) and (MATH/STAT 309 , MATH/STAT 431 , STAT 311 or MATH 531 ) and (MATH 322 , MATH 341 , MATH 375 , MATH 421 , or MATH 467 ), graduate/professional standing, or declared in Mathematics VISP

Satisfies

This course does not satisfy any prerequisites.

Credits

Not Reported

Offered

Not Reported

Grade Point Average
3.57

No change from Historical

Completion Rate
100%

No change from Historical

A Rate
40.54%

No change from Historical

Class Size
37

No change from Historical

Cumulative Grade Distribution

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

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