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  • Machine Learning

    15 - 17 November 2021

    Oeiras, Portugal

    Elixir node event
    Machine Learning With the rise in high-throughput sequencing technologies, the volume of omics data has grown exponentially in recent times and a major issue is to mine useful knowledge from these data which are also heterogeneous in nature. Machine learning (ML) is a discipline in which computers perform automated learning without being programmed explicitly and assist humans 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 explore their full potential. Machine learning can thus be very useful in mining large omics datasets to uncover new insights that can advance the field of bioinformatics. This 3 day course will introduce participants to the machine learning taxonomy and the applications of common machine learning algorithms to omics data. The course will cover the common methods being used to analyse different omics data sets by providing a practical context through the use of basic but widely used R libraries. The course will comprise a number of hands-on exercises and challenges where the participants will acquire a first understanding of the standard ML processes, as well as the practical skills in applying them on familiar problems and publicly available real-world data sets. Instructors: Vandrille Duchemin, University of Basel, CH Crhistian Cardona, University of Tuebingen, DE 2021-11-15 09:30:00 UTC 2021-11-17 18:00:00 UTC Pedro Fernandes Instituto Gulbenkian de Ciência, Oeiras, Portugal Instituto Gulbenkian de Ciência Oeiras Portugal 2780-156 Machine learning Instituto Gulbenkian de Ciência To register your interest, please send an e-mail to with "Machine Learning" in the subject line UNTIL November 11th 2021, stating the reason why you would be interested in attending this course in a single paragraph. ELIXIR PhD Studentspost-docsScientistsstudents 17 workshops_and_courses registration_of_interest Machine Learning, Introductory, Novice / Entry-level, Supervised learning, Unsupervised learning, Principal Component Analysis, K-means, Hierarchical Clustering, Decision Trees, Random Forest, Regression

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