CHE-5404: Machine Learning in Chemical Sciences and Engineering

Description: Data-driven machine learning models in chemical sciences and engineering for materials discovery, property prediction, anomaly, detection, process optimization. Data preprocessing, data management and visualization, clustering, classification/regression algorithms, uncertainty quantification, Bayesian statistics, and open access tools. Common pitfalls and practices. Potential bias and ethical issues in training and evaluation of machine learning models. Pre: Graduate standing.

Pathways: N/A

Course Hours: 3 credits

Corequisites: N/A

Crosslist: N/A

Repeatability: N/A