Differentiable Programming for End-to-end Optimization of Experiments - Lecture 5

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 optimizing experimental design and analysis in high energy physics research. Learn how differentiable programming can be applied to improve data collection, processing, and interpretation in complex particle physics experiments. Gain insights into cutting-edge methodologies that bridge the gap between theoretical models and experimental outcomes, potentially revolutionizing the way physicists approach data analysis and experimental design in the field of high energy physics.