The goal of this course is to give basic knowledge of stochastic differential equations useful for scientific and engineering modeling, guided by some problems in applications. The course treats basic theory of stochastic differential equations including weak and strong approximation, efficient numerical methods and error estimates, the relation between stochastic differential equations and partial differential equations, Monte Carlo simulations with applications in financial mathematics, population growth models, parameter estimation, and filtering and optimal control problems.
Course prerequisites: 
Multivariate calculus and a graduate course in probability and statistics, and exposure to ordinary differential equations.
Course instructor: 

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Course all programs: 
Applied and Computational Mathematics
Data Science
Financial Mathematics