CH EN 7703 - Bayesian Model Validation and Machine Learning

A course on the formal process of developing, validating and using science-based models. Specific topics covered include, why and how we make models, Bayesian parameter estimation, identification of experimental uncertainty, instrument modeling, surrogate modeling, analyzing model-form uncertainty, machine learning and decision theory. Students will implement Bayesian model validation and machine learning in a project selected from their own research.