Differentiable Programming for End-to-end Optimization of Experiments - Lecture 5
Offered By: International Centre for Theoretical Sciences via YouTube
Course Description
Overview
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.
Syllabus
Differentiable Programming for End-to-end Optimization of Experiments (Lecture-5) by Tommaso Dorigo
Taught by
International Centre for Theoretical Sciences
Related Courses
粒子世界探秘 Exploring Particle WorldShanghai Jiao Tong University via Coursera Dark Side of the Universe
World Science U Nature's Constituents
California Institute of Technology via World Science U Физика тяжелых ионов
National Research Nuclear University MEPhI via Coursera Particle Physics: an Introduction
University of Geneva via Coursera