Machine Learning (2/3)

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

Thursday 27 Apr 2017, 11:00 → 12:00 Europe/Zurich

222/R-001 (CERN)

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. Boosted by its industrial and commercial applications, the field of machine learning is quickly evolving and expanding. Recent advances have seen 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, for instance for particle identification, detector monitoring, and the optimization of computer resources. Modern machine learning approaches, such as deep learning, are only just beginning to be applied to the analysis of High Energy Physics data to approach more and more complex problems. These classes will review the framework behind machine learning and discuss recent developments in the field.

Kagan_Lecture2.pdf

Recording

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