Machine Learning for LHC Theory - Lecture 1

Explore the fundamentals of machine learning applications in Large Hadron Collider (LHC) theory through this comprehensive lecture by Tilman Plehn. Delve into the intersection of high energy physics and advanced data analysis techniques as part of the "Statistical Methods and Machine Learning in High Energy Physics" program. Learn how machine learning is revolutionizing the analysis of massive datasets generated by the LHC, aiding in the search for new physics beyond the Standard Model. Gain insights into classification, identification, characterization, and estimation strategies employed in LHC searches. Suitable for PhD students, postdoctoral researchers, and professionals in theoretical or experimental particle physics and astro-particle physics, this lecture serves as an essential introduction to the growing field of machine learning in high energy physics research.

Machine Learning for LHC Theory (Lecture 1) by Tilman Plehn