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Dec 26, 2024
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ENGR 6420 - Engineering Mathematics for Data Science
The course covers matrix algebra and decompositions, including eigenvalue and generalized eigenvalue problems, solving multivariate constraint and unconstraint optimization problems, gradient-based optimization for solving nonlinear optimization problems, and regression analysis. Concepts that are discussed include solving nonlinear optimization problems, first- and second-order gradient-based methods, estimating parameters for multiple linear regression and mechanistic first-principle models. The course also introduces important data science tasks: data analysis, regression, classification and presents application studies related to biomedical engineering.
Prerequisites/Corequisites: None.
When Offered: Fall annually
Cross Listed: BMED 6420
Credit Hours: 3
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