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SUMMARY:Training course: Machine learning for weather prediction
DTSTART:20220503T073000Z
DTEND:20220506T200500Z
DTSTAMP:20260722T135000Z
UID:indico-event-278@events.ecmwf.int
CONTACT:events@ecmwf.int
DESCRIPTION:\n\n\nThis four-day course focuses on machine learning for num
 erical weather prediction (NWP). This will include:\n\n\n	an overview on t
 he use of machine learning in Earth Sciences\,\n	the introduction into the
  most important machine learning methods that are relevant for Earth Scien
 ces\,\n	the introduction into software and hardware frameworks at ECMWF to
  facilitate the use of machine learning\,\n	and examples for the use of sp
 ecific machine learning tools across the weather and climate prediction wo
 rkflow and how they can be prepared for use in operational predictions.\n\
 n\nAs there are many general courses on machine learning available – inc
 luding free online courses – this course will have a particular focus on
  the use of machine learning in the domain of Earth Sciences.  \n\nAs wel
 l as lectures there will be discussion and hands-on sessions.\n\nMain topi
 cs\n\n\n	    An overview on the use of machine learning in Earth Scienc
 es\n	    Software for machine learning in NWP\n	    Hardware for mac
 hine learning in NWP\n	    Datasets that are available and data retriev
 al\n	    Regression and decision trees\n	    Deep learning\n	    
 Hybrid modelling\n	    High-performance computing readiness of machine 
 learning\n	    Examples of machine learning applications\n\n\nRequireme
 nts\n\nParticipants should have a good meteorological or a good machine le
 arning background\, or both. Participants should also have some limited ex
 perience with Python code and Jupyter notebooks. Basic experience with mac
 hine learning applications in Earth system sciences and the handling of Ea
 rth system data would be advantageous. Some practical experience in numeri
 cal weather prediction is an advantage.\n\nAll lectures will be given in E
 nglish.\n\n\nhttps://events.ecmwf.int/event/278/
IMAGE;VALUE=URI:https://events.ecmwf.int/event/278/logo-3547453490.png
LOCATION:ECMWF 
URL:https://events.ecmwf.int/event/278/
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