Machine Learning in High Energy Physics by Michael Kagan

Explore the intersection of machine learning and high energy physics in this comprehensive lecture by Michael Kagan. Delve into the application of advanced statistical methods and machine learning techniques in analyzing vast datasets from experiments like the Large Hadron Collider. Learn how these tools are revolutionizing the search for new physics and precision measurements in particle physics. Gain insights into classification, identification, characterization, and estimation strategies used in LHC searches. Discover the potential of deep learning and artificial intelligence in processing petabytes of data to uncover hints of physics beyond the Standard Model. Ideal for graduate students, postdoctoral researchers, and professionals in theoretical or experimental particle physics and astro-particle physics looking to enhance their skills in data-driven research methods.