Machine Learning 101: From particle astrophysics to ethical considerations

Ryan D. Martin,
Undergraduate Chair, Associate Professor
Department of Physics, Engineering Physics & Astronomy

Abstract

This talk will start with a basic introduction to machine learning to show how easy it is for anyone to get started using these tools. I will then discuss a few applications of machine learning. These range from our work to remove electronic noise in particle astrophysics experiments to pricing stock options and determining the deformation of a bouncing ping-pong ball. The talk will conclude with some thoughts, and hopefully some lively discussion, about the ethics of artificial intelligence and, more generally, training STEM students without including formal ethics training.

Timbits, coffee, tea will be served in STI A before the colloquium.