Optimization Methods for Data Science

Catalog description

Basic concepts in optimization are introduced. Linear optimization (linear and integer programming) will be introduced including solution methods like simplex and the sensitivity analysis with applications to transportation, network optimization and task assignments. Unconstrained and constrained non-linear optimization will be studied and solution methods using tools like Matlab/Excel will be discussed. Extensions to game theory and computational methods to solve static, dynamic games will be provided. Decision theory algorithms and statistical data analysis tools (Z-test, t-test, F-test, Bayesian algorithms and Neyman Pearson methods) will be studied. Linear and non-linear regression techniques will be explored. Prerequisite Course(s): Corequisites: DTSC 635