Neural Simulation-based Inference - Lecture 2

Explore the second lecture on Neural Simulation-based Inference, presented by Elham E Khoda and Aishik Ghosh as part of the Statistical Methods and Machine Learning in High Energy Physics program. Delve into advanced techniques for analyzing large-scale data in high energy physics research. Learn how machine learning and artificial intelligence are revolutionizing the field, particularly in the context of experiments at the Large Hadron Collider. Gain insights into classification, identification, characterization, and estimation strategies used in searching for new physics. This lecture, lasting 1 hour and 32 minutes, is part of a comprehensive program aimed at developing skills in deep machine learning frameworks for high energy physics applications. Suitable for PhD students and postdoctoral researchers in theoretical or experimental particle physics and astro-particle physics with programming experience and knowledge of event generation and data analysis tools.