Abstract

Machine learning, which builds on ideas in computer science, statistics, and optimization, focuses on developing algorithms to identify patterns and regularities in data, and using these learned patterns to make predictions on new observations. Machine learning is quickly evolving and expanding, with recent great success in the realms of computer vision, natural language processing, and broadly in data science. Many of these techniques have already been applied in particle physics, and modern machine learning approaches, especially deep learning,  are rapidly making their way into the analysis of High Energy Physics data to study more and more complex problems. These lectures will review the framework behind machine learning and discuss some recent developments in neural networks and deep learning.

Bio

Michael Kagan is a Staff Scientist at SLAC National Accelerator Laboratory.  His research focuses on studying the properties of the Higgs Boson on the ATLAS Experiment at the LHC, and on developing and applying machine learning methods in high energy physics.  Michael received his Ph. D. in physics from Harvard University, and his B.S. in physics and mathematics from the University of Michigan.