Data-Driven Modeling Science & Engineering

Catalog description

Statistical learning techniques and data assimilation for science and engineering applications. Focus is on the practical applications and the understanding of the assumptions underlying techniques, allowing students to learn the basics of useful tools for data-driven modeling and revisit their theoretical and practical underpinnings as needed. Topics may include supervised and unsupervised learning, regression, classification, importance sampling, ensemble forecasting, and Kalman Filtering. The codes R and Python will be used. (Same a HYDR 5020)