Introduction to Machine Learning

by Michael Kagan (SLAC National Accelerator Laboratory (US))

Wednesday 8 Jul 2026, 14:00 → Thursday 9 Jul 2026, 15:30 Europe/Zurich

593/R-010 - Salle 11 (CERN)

Abstract

Machine learning, which builds on ideas in computer science, statistics, and optimization, focuses on developing algorithms to identify patterns and regularities in data, and using these learned patterns to make predictions on new observations. Machine learning is quickly evolving and expanding, with recent great success in the realms of computer vision, natural language processing, and broadly in data science. Many of these techniques have already been applied in particle physics, and modern machine learning approaches, especially deep learning,  are rapidly making their way into the analysis of High Energy Physics data to study more and more complex problems. These lectures will review the framework behind machine learning and discuss some recent developments in neural networks and deep learning. 

Lecturer Biography

Michael Kagan is a Staff Scientist at SLAC National Accelerator Laboratory.  His research focuses on studying the properties of the Higgs Boson on the ATLAS Experiment at the LHC, and on developing and applying machine learning methods in high energy physics.  Michael received his Ph. D. in physics from Harvard University, and his B.S. in physics and mathematics from the University of Michigan.

Please note that pictures and videos might be taken during the event. The pictures and videos might be used for communication about the event. By joining the lecture, you are agreeing to being featured in these communication actions. 

Tutor Biography

Davide Valsecchi is a Postdoctoral Researcher at ETH Zurich, based at CERN with the CMS Experiment. He has been working at the intersection of particle physics and AI since his PhD, where he built one of the first Graph Neural Networks for CMS electron and photon clustering. Currently, Davide coordinates the CMS E/Gamma Physics Object Group and recently led the effort to integrate PyTorch into the CMS software stack. His active research focuses heavily on Generative Deep Learning—specifically using Normalizing Flows for unbinned likelihood fits, unfolding and detector calibration.

From this course, only the first lecture is publicly available:

Lecture 1: Machine Learning Fundamentals

This lecture will take place in the room 500/1-001 - Main Auditorium at 14:00-16:00 and is open to all CERN personnel. No registration is required.

Please note that the remaining lectures are reserved for registered CERN STEAM Academy students & CERN openlab summer students.