[Hackathon] Machine Learning Approaches

Calendrier
Quand :
4 septembre 2023 – 8 septembre 2023 Jour entier
2023-09-04T00:00:00+02:00
2023-09-09T00:00:00+02:00
Où :
Halle aux Farines, salle 442C
Graduate schools
formation
[Hackathon] Machine Learning Approaches @ Halle aux Farines, salle 442C

Come and take part in the Graduate School Translational Bioinformatics workshop on machine learning theory applied to problem solving in the medical and biological sciences.This workshop will include both theoretical and practical sessions with multiple examples.

The objective

The focus of the sessions will be on demonstrating the strength of these methods and the scope of application, but also on raising awareness of their limitations. Emphasis will be placed on the importance of managing best practices and using them under the appropriate conditions.

Target Audience

PhD students (or post-docs) and Master’s students interested in training in Machine Learning and/or Deep Learning, applied to biological or biomedical data. This workshop is also open to students in medicine, biology or other courses, with the requirement that they have a basic knowledge of a programming language such as R or Python.

Speakers from Université Paris Cité

  • Farah ELLOUZE, Clinical Bioinformatics laboratory, IHU Institut Imagine
  • Tatiana GALOCHKINA, DSIMB team, UMR-1134, MCU
  • Nicolas GARCELON, Data Science platform and Clinical Bioinformatics laboratory, IHU Institut Imagine
  • Jean-Christophe GELLY, DSIMB team, UMR-1134, MCU
  • Frédéric GUYON, DSIMB team, UMR-1134, IR
  • Romain NICOLLE, Clinical Bioinformatics laboratory, IHU Institut Imagine
  • Marc VINCENT, Data Science platform and Clinical Bioinformatics Laboratory, IHU Institut Imagine

 

Main Topics

Introduction to machine learning and the main concepts of supervised learning: loss function, model optimisation, model evaluation, under-fitting and over-fitting. Introduction to deep learning (DL) and its applications.
Convolution networks and their application to image processing. Practical work on medical images.
Advanced network architectures and language processing models: from recurrent networks to transformers. Entanglement of protein sequences.
Advanced topics in deep learning: graphical neural networks, federated learning, natural language processing (NLP).

 

Registration is free but mandatory (20 seats available)
This event is organized by Catherine ETCHEBEST (DSIMB team, UMR-1134) and Antonio RAUSELL (Clinical Bioinformatics laboratory, IHU Institut Imagine).

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