This 4 day school will guide across the various steps in Machine Learning, from the foundational concepts, through the deep learning and generative AI techniques, closely complemented by insights into the existing reporting (DOME Recommendations) and regulatory frameworks (EU AI Act).
Application deadline: 24.02.2025
Date: May 19th 2025 - May 23rd 2025
This virtual course will show how public bioimaging data resources, centred around the BioImage Archive, enable and enhance machine learning based image analysis. The content will explore a variety of data types including electron and light microscopy and miscellaneous or multi-modal imaging data at the cell and tissue scale. Participants will cover contemporary biological image analysis with an emphasis on machine learning methods, as well as how to access and use images from databases.
Applications close: 19 January 2025
Date: 31 March - 04 April 2025
DOME is a set of community-wide guidelines, recommendations and checklists, spanning these four areas (Data, Optimization, Model and Evaluation in Machine Learning) aiming to help establish standards of supervised machine learning validation in biology. The work was initially published in 2021 with more details on the DOME-ML page. This activity is part of the Machine Learning Focus Group.
Date: 24 September 2024, 13:00 CEST
This course will not make the participant an absolute expert in the complex and dynamic world of Deep-Learning. Still, it will aim to “break the ice” through the explaination and implementation of simple yet concrete, deep-learning models using the PyTorch library. Participants will be introduced to the basic building blocks of deep-learning models and how the main parameters are tuned and monitored to ensure the training of large models. This course is aimed at PhD students, post-docs and researchers in life sciences who already know about Machine Learning and would like to discover and start practising Deep Learning with PyTorch.
Application deadline: 31 October 2024
Date: 07 - 08 November 2024
This remote event focuses on applying FAIR principles to machine_learning models in the life sciences, enhancing their reproducibility and utility.
Why Participate?
The hackathon is ideal for researchers, data scientists, and developers at the intersection of AI/ML and life sciences.
Date: 20 - 21 June