This National Science Foundation (NSF) grant, awarded under the Computer and Information Science and Engineering program (CFDA 47.070), provides $239,095 to the University Corporation for Atmospheric Research (UCAR) to develop a series of educational modules that will teach university-level Earth systems science (ESS) students and early-career professionals the conceptual foundations and practical applications of machine learning techniques.
The project aims to bridge the gap between advanced machine learning theory and the use of low-code, real-world applications in ESS research. The three-part curriculum includes: 1) a self-paced conceptual introduction using a systems-thinking approach, 2) a self-paced, low-code module enabling learners to apply the concepts to relevant ESS data, and 3) a lab-based activity promoting group discussion, decision-making justification, and critical analysis of machine learning techniques and outputs. This integrated, flipped-classroom approach is designed to build necessary cyberinfrastructure literacy and skills without requiring additional ESS coursework. The project will foster critical thinking and appropriate, ethical usage of machine learning in the Earth systems sciences.
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