Machine Learning and AI for Climate Scie

Machine learning (ML) and artificial intelligence (AI) tools are quickly becoming part of the standard toolkit in climate research and problem solving. From improving climate model predictions to tracking emissions and mapping land use change with satellite data, ML methods are helping scientists make sense of enormous, complex datasets and evaluate potential climate solutions. As these tools spread across the field, research scientists and policymakers alike need a working understanding of what ML and AI can and cannot do. This course is a hands-on introduction to machine learning and AI with applications in climate science. Students will learn the fundamentals of building, training, and evaluating ML models, including data preprocessing, model selection, and performance metrics. Working with real climate and environmental datasets, students will gain practical coding experience in Python and a realistic sense of both the power and the limits of ML in climate research.