MIT EECS6.S891/6.S893/12.S992 AI for Climate Action |
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Spring 2026 |
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Description: Examines applications of artificial intelligence and machine learning to climate change mitigation, adaptation, and monitoring. Introduces the physical science of climate change, data-driven modeling and observation, and approaches for decision-making in domains such as climate modeling, biodiversity, and energy systems. Includes common (‘merged’) lectures on climate fundamentals followed by domain-specific sections (‘forks’) focusing on advanced methods such as physics-informed learning, data assimilation, and uncertainty quantification. Within each ‘fork’, students present, critique, and lead discussions of current research papers and develop a written research proposal applying machine learning methods to the track’s focus area. ‘Merged’ sessions later in the term synthesize lessons and foster exchange across domains. Both graduate and undergraduate students are encouraged to register.
Pre-requisites: 6.3900 or 6.8300/1 or 6.7960 or equivalent or permission of instructor.
Students should register for the subject number associated with the ‘fork’ in which they would like to participate:
beery at mit dot edu
abodner at mit dot edu
donti at mit dot edu
chaenayo at mit dot edu
xinkai at mit dot edu
pgovindu at mit dot edu