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RDATE:20231029T030000
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DESCRIPTION:The popularity of Machine learning (ML) and Deep learning (DL) 
 has sharply increased in recent years.&nbsp\; In ecology and evolution (E&
 amp\;E)\, ML and DL are used to process images and other complex data (e.g
 . for&nbsp\; automatic species identification) or to build predictive mode
 ls for conservation\, biodiversity assessment\, and risk estimation. Howev
 er\, despite their recent rise in popularity\, the inner workings of ML an
 d DL models are often perceived as opaque. For example\, is it still true 
 that ML and DL are good tools for predictions\, but statistics remains the
  choice when it comes to (causal) inference?&nbsp\;&nbsp\;Here\, I provide
  an overview of the principles of ML and DL\, how these tools differ from 
 traditional statistical tools\, and what&nbsp\; that means when applying M
 L. I then discuss why and when ML and DL models excel\, and where traditio
 nal statistical methods are preferable\, highlighting current and emerging
  applications for ecological problems. Finally\, I summarize emerging tren
 ds\, particularly scientific and causal ML\, that could significantly impa
 ct ecological data analysis in the future.\n*** invited by BayCEER members
  Prof. Dr. Lisa H&uuml\;lsmann\, Dr. Magdalena Mair and Dr. Matthias Schot
 t
DTSTART;TZID=Europe/Berlin:20220728T000000
DTEND;TZID=Europe/Berlin:20220728T235959
LOCATION:H8/GEO + online
SUMMARY:Maximilian Pichler\, Group for Theoretical Ecology\, University of 
 Regensburg (Homepage): Machine Learning and Deep Learning - Opportunities 
 and limitations for ecology
TRANSP:TRANSPARENT
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