Machine Learning for Chemical and Materials Engineers. 3 credits, 3 contact hours

Catalog description

Prerequisites: Undergraduate degree in either Chemical, Mechanical, Electrical or Biomedical Engineering, or in Physics or Chemistry. This course builds upon foundational knowledge in mathematical and statistical tools or data science and data visualization to learn and apply machine learning approaches for solving problems in Engineering with emphasis on applications in Chemical and Materials. As an engineering course, the emphasis is on case studies involving problem solving to augment mechanistic methods. It will include artificial intelligence (AI) topics such as machine learning and deep learning via methods such as classification, clustering, and neural networks (NNs), along with robust estimation including validation and error quantification. Class projects will concern case studies from a broad range of disciplines and applications to chemical and materials engineering problems.