Machine Learning for the Earth and Environment

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. The course will familiarize students with a cross-section of common machine learning models and algorithms with an emphasis on developing practical skills for working with data. Topics covered in the course 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. The intended audience for this course 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.