Coursera Flash Sale
40% Off Coursera Plus for 3 Months!
Grab it
Explore advanced statistical methods and machine learning applications in high energy physics through this comprehensive workshop series organized by the International Centre for Theoretical Sciences. Delve into cutting-edge techniques essential for analyzing the massive datasets generated by the Large Hadron Collider and other precision experiments in particle physics. Master deep learning architectures including DeepSets, Graph Neural Networks, and Transformers through detailed lectures and hands-on coding sessions led by experts like Sanmay Ganguly. Learn statistical methods for data analysis, point estimation, and fitting techniques with practical ROOT exercises guided by Tommaso Dorigo. Discover neural simulation-based inference methods and generative models for physics applications through sessions with Elham E Khoda and Aishik Ghosh. Understand machine learning applications in LHC theory with Tilman Plehn, and explore differentiable programming for end-to-end experiment optimization. Examine anomaly detection using autoencoders, machine-learned symmetries, and ML applications for Higgs physics research. Gain insights into the future of data-driven high energy physics research as the field prepares for the High Luminosity LHC era, which will generate petabytes of data requiring sophisticated machine learning analysis techniques. Participate in project presentations and brainstorming sessions designed to foster collaborative research in this rapidly evolving intersection of artificial intelligence and particle physics.
