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PRODID:-//pretalx//pretalx.earthmonitor.org//UNKX8N
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DTSTART:20001029T030000
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BEGIN:VEVENT
UID:pretalx-opengeohub-summer-school-2025-UNKX8N@pretalx.earthmonitor.org
DTSTART;TZID=Europe/Amsterdam:20250905T090000
DTEND;TZID=Europe/Amsterdam:20250905T100000
DESCRIPTION:Machine learning has become an important toolbox for analyzing 
 complex Earth observation data to derive information from the raw data. In
  particular\, supervised deep learning has achieved great success in solvi
 ng EO tasks where the relationship between input and output is not clearly
  understood. However\, applications with limited reference data cannot dir
 ectly benefit from advances in supervised deep learning. This lecture will
  first introduce the concepts of supervised deep learning and then provide
  an overview of research in the field of self-supervised learning (SSL)\, 
 which aims to learn transferable representations (i.e.\, features) from un
 labeled data.
DTSTAMP:20260624T124924Z
LOCATION:HugoTECH
SUMMARY:Learning Representations: From Engineering Features to Engineering 
 Pretext Tasks - Nico Lang
URL:https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/UNK
 X8N/
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