Wednesday, 27 July, 2016 -
15:00 to 16:30
NOTE: Please bring your laptop for hands-on excercices.
Brief description: These two lectures will cover the fundamentals of machine learning theory and as well as it’s many practical applications in fields ranging from computer science to particle physics. The lectures will provide an introduction to basic and advanced machine learning methods, such as boost decision trees, neural networks, deep learning and others, and will contain hands-on examples that illustrate the methodology and available software for solving a variety of problems in these domains.
Speakr's short bio: I am a particle physicist and researcher from the University of Florida, working in the applied field of machine-learning in particle physics for the past 12 years, developing novel algorithms and applications to high-energy physics data analysis. I am a member of the Compact Muon Solenoid (CMS) experiment and organizer of the Inter-experimental LHC Machine-Learning Working Group, focusing on development of machine-learning applications and software for particle physics experiments.
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