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SUMMARY:Machine learning basics ST-LE-2026-17
DTSTART:20260430T080000Z
DTEND:20260430T100000Z
DTSTAMP:20260513T090100Z
UID:indico-event-108@events.gfbio.org
CONTACT:data-train@vw.uni-bremen.de\;+49 (421) 218 60043
DESCRIPTION:Speakers: Lena Happ (Alfred-Wegener-Institut)\n\nMotivation H
 ave you ever considered incorporating machine learning techniques into you
 r research but felt deterred by its perceived complexity? The success of m
 achine learning in quite complicated tasks might create the misconception 
 that it is exclusively for experts. At the same time\, machine learning is
  sometimes perceived as a magic tool\, seemingly capable of solving any ta
 sk. In this case the inherent limitations of machine learning are missed b
 ecause an understanding of its fundamentals is lacking. However\, most mac
 hine learning techniques are based on principles that can be explained on 
 many levels of complexity.Learning contents In this talk we are going to 
 explain the general concepts of machine learning on a basic level. Instead
  of delving into the intricacies of specific techniques\, we'll shed light
  on the distinctions and commonalities among various algorithms. Specifica
 lly\, we'll explore the principles of supervised learning starting from li
 near regression. With regard to unsupervised learning we will discuss how 
 clustering algorithm differ in their criteria to identify clusters in data
 . Additionally\, we may touch upon dimensionality reduction techniques.Lea
 rning outcomes The goal is to empower you with a foundational understandi
 ng of machine learning\, accompanied by an awareness of its limitations\, 
 encouraging you to explore its potential without feeling overwhelmed by co
 mplexity.Prior knowledge---Further reading---\n\nhttps://events.gfbio.org/
 event/108/
LOCATION:Room 2.2070/2.2090\, 2nd floor + Zoom (hybrid)
URL:https://events.gfbio.org/event/108/
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