Machine Learning for LHC Theory - Lecture 2

Explore the second lecture in the series on Machine Learning for LHC Theory, delivered by Tilman Plehn at the International Centre for Theoretical Sciences. Dive into advanced statistical methods and machine learning techniques applied to High Energy Physics, particularly focusing on their relevance to Large Hadron Collider (LHC) research. Gain insights into how these cutting-edge approaches are revolutionizing data analysis in the field, enabling more precise measurements of the Higgs boson and potentially uncovering hints of new physics. Benefit from expert instruction on deep machine learning and artificial intelligence frameworks specifically tailored for HEP applications. Enhance your understanding of classification, identification, characterization, and estimation strategies crucial for LHC searches. Ideal for PhD students, postdoctoral researchers, and professionals in theoretical or experimental particle physics and astro-particle physics looking to expand their knowledge in this rapidly evolving area of study.

Machine Learning for LHC Theory (Lecture 2) by Tilman Plehn