Differentiable Programming for End-to-end Optimization of Experiments - Lecture 6

Explore differentiable programming for end-to-end optimization of experiments in this lecture by Tommaso Dorigo, 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, with a focus on machine learning applications. Gain insights into the future of data-driven approaches in particle physics, including strategies for processing petabytes of data from the Large Hadron Collider and other precision experiments. Learn how deep machine learning and artificial intelligence frameworks are revolutionizing the search for new physics and the precise measurement of Higgs boson properties. Suitable for PhD students, postdoctoral researchers, and professionals in theoretical or experimental particle physics and astro-particle physics with programming experience in Python and C++, as well as familiarity with event generation and data analysis tools.

Differentiable Programming for End-to-end Optimization of Experiments (Lecture 6) by Tommaso Dorigo