Optimization methods for machine learning and data science applications in business, engineering, sciences. Core formulations and algorithms for continuous, discrete, dynamic optimization problems. Why algorithms work, and implementation of methods. Course Information: 3 undergraduate hours. 4 graduate hours. Prerequisite(s): IDS 371 or FIN 330 or ECON 300 or ACTG 392 or STAT 382 or STAT 481 and knowledge of programming at the level of IDS 201 or equivalent.