Course Schedule and Catalog

Long Title: INDUSTRIAL AND APPLIED DATA SCIENCE AND CONTROL THEORY

Department: *Computational & Applied Math*

Grade Mode: Standard Letter

Language of Instruction: Taught in English

Course Type: Lecture

Credit Hours: 3

Description: This graduate level course presents a pragmatic introduction to the foundational theory of data science and optimal control along with multiple practical applications. It includes modern (post-1990) aspects of data science driven by massively more data and computer power such as deep neural networks. Dynamical systems and optimal control methods are deeply impacted by these developments, and the course includes relevant sections on nonlinear control and reinforcement learning. It is supplemented by practical programming exercises to be completed every week by all students. Several industrial-strength applications from the energy sector are discussed in appropriate detail. Recommended Prerequisite(s): Equivalent of advanced course work in computer programming (e.g. COMP 321), calculus (e.g. MATH 212), statistics or probability theory (e.g. STAT 331), linear algebra (e.g. CAAM 334 or 335). Proficiency in MATLAB (course programming language) or Python (alternative to MATLAB available to course participants).