About
Table of contents
About
This course is created by members of the volunteer association Climage Change AI. We hope these course materials will help climate scientist, climate industry engineers, or anyone new to programming and machine learning learn techniques for applying AI to Climate Change problems.
Lecture
Lecture materials are available along with python notebooks in the course materials. Instructions for how to download course materials can be found on the Modules page.
Resources
Rolnick, D. Donti, P.L., Kaack, L.H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A.S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., Luccioni, A., Maharaj, T., Sherwin, E.D., Mukkavilli, S.K., Kording, K.P., Gomes, C., Ng. A.Y., Hassabis, D., Platt, J.C., Creutzig, F., Chayes, J., Bengio, Y. (2019) Tackling climate change with machine learning.
Huybers, P (2006) Early Pleistocene glacial cycles and the integrated summer insolation forcing. Science, 313, 508–511.
Archer, D. (2009) The long thaw: How humans are changing the next 100,000 years of Earth’s climate. Priceton University Press.
MacKay, D. (2009) Sustainable energy - without the hot air. UIT Cambridge Ltd.
Gates, B. (2021) How to avoid a climate disaster. The solutions we have and the breakthroughs we need. Penguin Random House.
Assignments
In the course materials GDrive folder, “assignment” notebooks, notebooks with key concepts left blank for students to fill in, can be found in the ccai/course/notebooks/assignments folder. Solutions can be found in the ccai/course/notebooks/solutions folder.