Data-driven & Reduced Complexity Modeling

3 credits · College of Engineering catalogue

Advisory Prerequisite: Good background in Linear Algebra, Probability and Statistics. Enforced Prerequisite: None. (3 credits) Review of Linear Algebra and Probability; Linear and Kernel Regression; Discovery of dynamical systems and PDEs from data; Statistical Learning Theory; Dimensionality reduction & Manifold Learning; Variational Inference & Variational Autoencoders; Compression/Sensing/Reconstruction; Projection-based Reduced Order Modeling.