Predictive Analytics for Systems Engineers

3 credits

Focuses on predictive modeling in engineering systems using data-driven methods. Topics include dimensionality reduction, functional data analysis, model selection, and supervised (e.g., kernel methods, Generalized Additive Model, Convolutional Neural Networks, Recurrent Neural Networks) and unsupervised learning. Applications span manufacturing, logistics, healthcare, and transportation. Covers causal inference and time series forecasting. Course overlaps with: CET 521. Prerequisite: IND E 527 or equivalent. Offered: W.