Machine Learning for Climate Science and

The application of Machine Learning (ML) to climate science and environmental sustainability has become increasingly popular in recent years, promising to revolutionize how we analyze and address critical environmental challenges. This course will introduce students to the fundamental concepts and methods of ML, emphasizing their practical applications to climate science and environmental sustainability efforts.

Students will gain both theoretical knowledge and practical skills through hands-on experience with machine learning methods and coding. The course is designed to provide familiarity with the design, implementation, and evaluation of machine learning models towards addressing specific problems in climate science and sustainability. By working with real-world datasets, students will develop a deeper understanding of both the capabilities and limitations of ML tools in climate research and for evaluating environmental sustainability solutions. This course will cover essential topics such as data preprocessing, model selection, evaluation metrics, and the ethical implications of ML in climate science.

As ML tools become increasingly important to these application areas, this course will be invaluable for those looking to interact with scientists and engineers, manage scientific projects, and develop policies in the realm of climate science and sustainability.