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.
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