EEPS / DATA 1720: Tackling Climate Change with Machine Learning
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; Climate misinformation.
- Machine learning topics may include: Physics-informed learning and emulators; Explainable AI; Uncertainty quantification; Image super-resolution; Graph neural networks, Policy optimization.
EEPS / DATA 1340: Machine Learning for the Earth and Environment (previously EEPS 1960D)
Spring 2021 website | Spring 2022 website | Fall 2023 website
This course introduces science students to modern data science tools for exploratory data analysis, predictive modeling with machine learning, and scalable algorithms for big data. Familiarize students with a cross-section of common machine learning models and algorithms emphasizing developing practical skills for working with data. Topics covered may include dimensionality reduction, clustering, time series modeling, linear regression, regularization, linear classifiers, ensemble methods, neural networks, model selection and evaluation, scalable algorithms for big data, and data ethics. The course will present case studies of these tools applied to problems in the Earth sciences. Intended audience is advanced undergraduate and graduate students in Earth, Environmental and Planetary Sciences or other physical science disciplines. Students will practice and develop their skills in data science through a hands-on project on a topic of their choice. This course is taught using the Python programming language.
DATA 1010: Probability, Statistics and Machine Learning
In this course we will introduce the mathematical methods of data science through a combination of computational exploration, visualization, and theory. We will learn scientific computing basics, topics in numerical linear algebra, mathematical probability (probability spaces, expectation, conditioning, common distributions, law of large numbers and the central limit theorem), statistics (point estimation, confidence intervals, hypothesis testing, maximum likelihood estimation, density estimation, bootstrapping, and cross-validation), and machine learning (regression, classification, and dimensionality reduction, including neural networks, principal component analysis, unsupervised learning, Bayesian methods, and graphical models).
DATA 1010 was previously a required course for the Sc.M. program in Data Science at Brown University.