Machine Learning for Chemical Engineers

(3 Credit Hours) · Undergraduate

Machine Learning (ML) is an important technological tool affecting society in myriad ways. Chemical engineering is not the exception. Students will be exposed to multiple examples within the chemical engineering discipline to appreciate the potential of ML as well as its limitations. The course is structured to provide a practical introduction to machine learning for chemical engineers. Topics to be covered include regression, supervised learning, unsupervised learning, feature extraction and other tools relevant to chemical and molecular engineering (SMILES, RDKit, etc.). The course will emphasize practical programming skills using Python implementations and will use case studies in chemical engineering. Students should have strong math and Python skills. Students who have already taken classes such as Numerical Methods and Statistical Analysis, Linear Algebra, Calculus, and Thermodynamics should have the necessary background to be successful in this course.