Advanced Computational Methods credit:

3 Hours

Preparation for advanced computational work, building directly upon the skills acquired in ATMS 305 or equivalent. Sophisticated techniques for handling, analyzing, and modeling complex weather and climate datasets are presented. The course emphasizes practical application, covering advanced data manipulation of domain-specific, multi-dimensional datasets (e.g., climate reanalysis and simulations), relevant machine learning methods (e.g., regression and dimensionality reduction), foundational GPU computational tools (e.g., PyTorch), and AI-driven weather-climate forecasting. Prerequisite: ATMS 305 or consent of the instructor.