Topic outline

  • General

    Machine Learning for bioinformatics and computational biology

    Zurich, 21-25 November 2016

    Location: University of Zurich, Irchel campus, room Y55-L-06/08

    This page is addressed to registered participants. To access the course description and the application form, please click here.

    For any assistance, please contact training@sib.swiss.

    • Technical / software prerequisite

      Laptop with recent versions of R installed, 3 GB of free disk space

      You need to install MXNET (an R package for deep learning) BEFORE the course. The installation instructions can be found here: http://mxnet.io/get_started/setup.html

      • General Programme

        Days 1 & 2: 9h - 17h
        Introduction to Machine Learning: concepts and methods

        Dr Frédéric Schütz, University of Lausanne and Bioinformatics Core Facility Group, SIB Swiss Institute of Bioinformatics, Lausanne.

        Days 3 to 5: Applications with use cases:

        Day 3: 9h - 17h
        Deep learning methods and cancer genomics
        Theory: Introduction to deep learning, standard architectures: MLP, autoencoders, recurrent nets.
        Practical: use of standard data sets and application to genomic data
        The day will be composed of an alternance of teaching and exercises
        Prof Ivo Kwee, Bioinformatics Core Unit, SIB and Institute of Oncology Research, Bellinzona, Switzerland

        Day 4: 9h - 17h
        Feature selection for biomarker discovery from high-content -omics data

        Prof Carlos Peña-Reyes, Computational Intelligence for Computational Biology, HEIG-VD/SIB Swiss Institute of Bioinformatics, Yverdon, Switzerland

        Day 5: 9h - 17h
        Machine Learning and metagenomics to study microbial communities

        Dr Luis Pedro Coelho, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.