Teaching machine learning with an application in collider particle physics

Teaching machine learning with an application in collider particle physics

We describe a hands-on introduction to deep learning in particle physics, performed during the 5th INFIERI school in Wuhan, China. We presented fundamental machine learning concepts to students from diverse backgrounds in physics and computing, and prepared them to apply these techniques to solve an...

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Veröffentlicht in:Journal of instrumentation 2020-09, Vol.15 (9), p.C09011-C09011
Hauptverfasser: Benato, L., Connor, P.L.S., Kasieczka, G., Krücker, D., Meyer, M.
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Machine learning
Particle physics
Quarks
Teaching
Teaching machines
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container_end_page C09011
container_issue 9
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container_title Journal of instrumentation
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creator Benato, L.
Connor, P.L.S.
Kasieczka, G.
Krücker, D.
Meyer, M.
description We describe a hands-on introduction to deep learning in particle physics, performed during the 5th INFIERI school in Wuhan, China. We presented fundamental machine learning concepts to students from diverse backgrounds in physics and computing, and prepared them to apply these techniques to solve an example problem from particle physics (hadronic top quark tagging). We exploited the simplicity of tools like Jupyter notebooks, and the user-friendly approaches of data science libraries such as Keras with TensorFlow.
doi_str_mv 10.1088/1748-0221/15/09/C09011
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subjects Machine learning
Particle physics
Quarks
Teaching
Teaching machines
title Teaching machine learning with an application in collider particle physics
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