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DTSTAMP:20260614T172818Z
UID:976d6c5f-f22e-4d07-9578-4cb4b41825b9
DTSTART:20190909T090000Z
DTEND:20190909T170000Z
DESCRIPTION:Machine learning has emerged as a discipline that enables compu
 ters to assist humans in making sense of large and complex data sets. With
  the drop-in cost of sequencing technologies\, large amounts of omics data
  are being generated and made accessible to researchers. Analysing these c
 omplex high-volume data is not trivial and the use of classical tools cann
 ot explore their full potential. Machine learning can thus be very useful 
 in mining large omics datasets to uncover new insights that can advance th
 e field of medicine and improve health care.\n\nThe aim of this tutorial i
 s to introduce participants to the Machine learning (ML) taxonomy and comm
 on machine learning algorithms. The tutorial will cover the methods being 
 used to analyse different omics data sets by providing a practical context
  through the use of basic but widely used R and Python libraries. The tuto
 rial will comprise a number of hands on exercises and challenges\, where t
 he participants will acquire a first understanding of the standard ML proc
 esses as well as the practical skills in applying them on familiar problem
 s and publicly available real-world data sets.
LOCATION:University of Basel\nKollegienhaus\nPetersplatz 1\nCH-4001 Basel
SUMMARY:Introduction to Machine Learning
URL;VALUE=URI:https://fpsom.github.io/IntroToMachineLearning/
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