Recent advancements in machine learning made it a major tool in big data analytics. We daily face applications of deep learning in image, speech and video recognitions, pledging for the efficiency of these methods in learning various complex tasks from data itself. With much care, advanced machine learning techniques are growingly used in Science, yielding better results than otherwise possible to Physicists. In this lecture, we will introduce the basics of machine learning and illustrate collider-specific aspects of deep learning by reviewing state-of-the-art applications of machine learning in high energy physics. The introduction to Machine Learning from Glen Cowan https://indico.cern.ch/event/1132551/ is highly recommended and repeated content will be avoided wherever possible.
Vlimant is a research scientist in the Physics Mathematic and Astronomy department at the California Institute of Technology. Vlimant holds a Master in Quantum Mechanics from Ecole Normal Superieure of Paris and a Ph.D in particle physics from the Pierre et Marie Curie University. Vlimant is taking leading roles in the High Energy Physics community effort of developing deep learning and quantum computing applications for particle physics, actively involved with activities at CERN, the Compact Muon Solenoid collaboration and high performance computing facilities worldwide. Vlimant is the co-coordinator of the machine learning group in CMS. Vlimant is specialty section chief editor of “Big data and AI in High Energy Physics” in the frontiers in big data journal.