Instructor: Karianne Bergen, Assistant Professor of Data Science and EEPS and Assistant Professor of Computer Science
Faculty Office hours: by appointment (sign up via Calendly link on Canvas/syllabus)
Teaching Assistant: John Nicklas, PhD student in Earth, Environmental and Planetary Sciences
Class meetings: Tuesday & Thursday 10:30-11:50am @ TBD
This course will explore recent work that leverages machine learning (ML) as a tool for tackling climate change, with a focus on climate science and climate adaptation. We will discuss how modern machine learning can be used to assess, understand and respond to projected climate extremes, natural disasters, and environmental change. The target audience for this course is advanced undergraduate students or graduate students who are interested in using ML and AI to address high-impact global issues. Students will read and discuss recent research papers on ML for Climate and complete an original project as a member of a multidisciplinary team.
Climate themes may include: Climate models and predictions; Extreme weather and natural disasters; Farms and forests; Oceans and marine ecosystems.
Machine learning topics may include: Physics-informed learning and emulators; Explainable AI; Uncertainty quantification; Image super-resolution; Graph neural networks.
This course is offered jointly by the Department of Earth, Environmental and Planetary Sciences and the Brown Data Science Initiative.
EEPS-DATA 1720 is a seminar course centered around presentations and discussions of recent research papers. Introductory lectures in the first weeks of the course will introduce students to basics of climate change, with additional mini-lectures given by the instructor throughout the course. Students will work in interdisciplinary teams on a course project.
One of the following is required:
Familiarity with fundamentals of machine learning / deep learning through coursework [e.g. CSCI 1420, CSCI 1470 (best preparation), DATA 1030, EEPS 1340/1960D], research, or project experience, OR
A background in climate science (e.g. EEPS 1430, 1510 or 1520), a solid statistical and computational background, and permission of the instructor.
Basic programming experience in Matlab, Python, R, or any high-level programming language is required.
Students should have an interest in climate science, adaptation and mitigation, but prior coursework in Earth and/or environmental science is not necessary.
EEPS-DATA 1340 (formerly EEPS 1960D) is an introductory data science course geared toward students in the Earth and physical sciences. EEPS 1340 assumes no prior knowledge of machine learning (ML). Students will develop hands-on data science skills through programming exercises in Python with the scikit-learn package and an applied ML project on a topic of the student's choice. The course will draw from examples in Earth, Environmental and Planetary sciences, so an interest in these topics is recommended but prior coursework in EEPS is not necessary. EEPS-DATA 1340 was offered in Fall 2023 (anticipated next offering: Spring 2025).
EEPS-DATA 1720 (Tackling Climate Change with Machine Learning) is course designed for students with prior experience in ML (see prerequisites or consult the instructor) who are looking to deepen their knowledge of ML tools and learn about how these tools can advance climate research. Students will read and discuss recent research articles (from top climate journals and ML conferences) on ML for climate-related applications. Students will also complete a group project to gain deeper knowledge of a climate application and/or advanced ML tools. A desire to learn more about climate science and climate change solutions is required; prior coursework in climate science is not required but may be helpful. EEPS-DATA 1720 will be offered in Spring 2024 (anticipated next offering: Spring 2026).