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Machine Learning

<!--HTML-->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 commerci...

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Autor principal: Kagan, Michael Aaron
Lenguaje:eng
Publicado: 2017
Materias:
Acceso en línea:http://cds.cern.ch/record/2261385
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author Kagan, Michael Aaron
author_facet Kagan, Michael Aaron
author_sort Kagan, Michael Aaron
collection CERN
description <!--HTML-->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.
id cern-2261385
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2017
record_format invenio
spelling cern-22613852022-11-03T08:15:30Zhttp://cds.cern.ch/record/2261385engKagan, Michael AaronMachine LearningMachine LearningAcademic Training Lecture Regular Programme<!--HTML-->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.oai:cds.cern.ch:22613852017
spellingShingle Academic Training Lecture Regular Programme
Kagan, Michael Aaron
Machine Learning
title Machine Learning
title_full Machine Learning
title_fullStr Machine Learning
title_full_unstemmed Machine Learning
title_short Machine Learning
title_sort machine learning
topic Academic Training Lecture Regular Programme
url http://cds.cern.ch/record/2261385
work_keys_str_mv AT kaganmichaelaaron machinelearning