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Explore machine-learned symmetries in high energy physics through this comprehensive lecture by Konstantin Matchev. Delve into advanced statistical methods and machine learning techniques applied to particle physics research. Gain insights into how symmetries can be discovered and leveraged using data-driven approaches. Learn about cutting-edge applications of deep learning and artificial intelligence in analyzing large datasets from experiments like the Large Hadron Collider. Understand the potential for machine learning to uncover new physics and precisely measure properties of fundamental particles. Suitable for PhD students, postdocs, and researchers in theoretical or experimental particle physics and astrophysics with programming experience and knowledge of event simulation tools.
