Machine Learning: Concepts and Use Case in Mapping the Universe
Peter Williams is a Busti, NY local who graduated from State University of New York’s Jamestown Community College (SUNY JCC) in 2016. He went on to earn a Bachelor’s degree in theoretical physics from SUNY Geneseo and a Master’s degree in theoretical physics from The Ohio University. He now teaches physics and engineering courses at SUNY JCC. During Peter’s time in graduate school, he was a member of the Dark Energy Spectroscopic Instrument (DESI) collaboration. He spent time with machine learning as part of a group working on observational systematics in mock data.
Artificial Intelligence in the form of Machine Learning is becoming more commonplace. Many seek a basic understanding of the concepts and terminology involved. This presentation by Peter Williams provides an overview of the types of machine learning algorithms, how they work, and many of their applications. It showcases the use of a neural network to assist the DESI collaboration in their mission to create the largest ever 3D map of the universe.
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