Uncertainty Quantification and Scientific Machine Learning

3 credits · Graduate Programs

An introduction to predictive modeling and uncertainty quantification for scientific and engineering applications. Key concepts include: Bayesian interference, stochastic differential equations, Monte Carlo methods for uncertainty propagation; supervised learning techniques including Bayesian regression, Gaussian processes, and deep learning; unsupervised methods; state space models with applications such as mechanical vibrations. A hands-on approach to data science and machine learning is emphasized over theoretical derivations.