machine learning environmental study

MANAGER: David Schneidinger


Learning Objectives: This project aims to be an introduction for physicists interested in machine learning and its applications to physics. Fall quarter will be focused on just trying to give everyone a grasp on the fundamentals of machine learning. Group members will work on small ML projects of their choosing, either individually or in small groups, as we all work together to learn and understand the subject. Learning python-based machine learning libraries (PyTorch, TensorFlow, Scikit-learn). Learn python based data visualization methods (Paraview) to analyze trends.

For the winter and spring quarters, we plan on using environmental data from the EPA in order to train our AI, but there will be lots of freedom as to what direction the project will take at point.

Prerequisites: There are no strict requirements for prior knowledge or experience, but some introductory programming knowledge and basic understanding of mechanics is preferred. Most importantly, come willing to learn!