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DTSTAMP:20260615T003458Z
UID:caeabf7b-0c55-475c-810f-e199e5d4420b
DTSTART:20211115T093000Z
DTEND:20211117T180000Z
DESCRIPTION:With the rise in high-throughput sequencing technologies\, the 
 volume of omics data has grown exponentially in recent times and a major i
 ssue is to mine useful knowledge from these data which are also heterogene
 ous in nature. Machine learning (ML) is a discipline in which computers pe
 rform automated learning without being programmed explicitly and assist hu
 mans to make sense of large and complex data sets. The analysis of complex
  high-volume data is not trivial and classical tools cannot be used to exp
 lore their full potential. Machine learning can thus be very useful in min
 ing large omics datasets to uncover new insights that can advance the fiel
 d of bioinformatics.\n\nThis 3 day course will introduce participants to t
 he machine learning taxonomy and the applications of common machine learni
 ng algorithms to omics data. The course will cover the common methods bein
 g used to analyse different omics data sets by providing a practical conte
 xt through the use of basic but widely used R libraries. \nThe course will
  comprise a number of hands-on exercises and challenges where the particip
 ants will acquire a first understanding of the standard ML processes\, as 
 well as the practical skills in applying them on familiar problems and pub
 licly available real-world data sets.\n\nInstructors: \nVandrille Duchemin
 \, University of Basel\, CH\nCrhistian Cardona\, University of Tuebingen\,
  DE
LOCATION:Instituto Gulbenkian de Ciência
SUMMARY:Machine Learning
URL;VALUE=URI:http://biodata.pt
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