CLIMATE PREDICTION CHALLENGES WITH MACHI

This course is a project-based learning (PBL) course where teams of climate science and data science students collaborate to create machine learning predictive models for challenges inspired by ongoing climate data science research. Students from different background will apply their prior knowledge, work together and teach each other in high-paced collaborative projects. Through a sequence of mini-projects, i.e., “challenges”, this course provides students a deeper understanding of using machine learning for climate science and support predictive capabilities. It provides training on a broad set of practical skills for climate data science research (e.g., handling geoscience data formats, data curation, cleaning and transformation, building ML workflow, and collaboration using cloud computing resources, Git and/or GitHub). It will also offer discussions on the opportunities and challenges of using climate science and projections in decision processes.

Minimal formal instruction on statistics, data science, machine learning, or climate science will be given. Project cycles run every 4 weeks, where we will have mini-group data projects. Groups will be formed randomly with students from both climate science and data science background. Project products will be peer-reviewed, in addition to evaluation by the instructional team.