Machine Learning for bioinformatics and computational biology
Zurich, 21-25 November 2016
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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
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.