Welcome to the third edition of the Machine Learning @ INFN (ML_INFN) advanced level hackathon, dedicated to INFN affiliates. This edition is hosted at INFN Sezione di Bari.
Notably, it is the first Hackathon to happen in Person, so please apply only if you are planning to come to Bari. The logistics allow for ~ 20 participants.
You can check the first edition here and the second edition here, with most of the talks attached as video files.
The mandatory registration process will be open from Monday October 12th to Monday October 24th.
In case of a number of registrations exceeding the available positions, the applications will be ranked and selected on the basis of the scientific CV of the applicants.
The successful applicant will be informed by November 1st. Please do not book hotel/flight before a positive confirmation.
The course is to be considered as "advanced level" for Machine Learning topics. The hackathon will be organized over 4 days, distributed as
- Infrastructure and tools for Machine Learning at INFN
- Models constitued of multiple neural networks
- Models for data beyond tabular format
- Ongoing developments towards the future of Machine Learning
The afternoons of days 2 and 3 will be devoted to experimenting the various methods and architectures with the help of tutors.
Upon registration, users will be asked to express their preferences for a one of the use cases offered. We will try to
- whenever possible, satisfy the preference in the order given
- try to form groups with students with the full range of proficiencies, in order to allow for self-tutoring inside the groups
The list of available use cases for the hackathon are currently (there could be additions depending on the registration process and on the status of other opportunities):
- Lung segmentation on Chest X-Ray images with U-Net
- Domain adaptation to train model-indepentent classifiers in High Energy Physics
- Graph Neural Networks and Transformers
- Explainable Artificial Intelligence (XAI)
More information is available in the Hackathon exercises tab on left menu.
Technical: access to INFN Cloud provided resources
NOTE: users will use INFN-Cloud resources and this does not require any specific INFN-Cloud authorization.
Please read and comply with the recommendations in the side menu "technical prerequisite".
Machine learning related knowledge
The hackathon is to be considered as advanced level for Machine Learning. Hence, students are expected to have a starting level of understanding of machine learming and on the technologies it implies. For exemple
- good fluency in Python:
- program flow, definition and use of variables
- definition and utilization of functions
- package management (import, install via pip)
- fluency in numpy:
- arrays, basic operations, reshaping
- fluency on matplotlib:
- how to define and plot a basic figure
- fluency on online python notebooks
- (like SWAN, Jupyter, Google Colab)
- basic concepts of Deep Learning
- (e.g.) working principle of dense and convolutional layers
- epochs, batches, optimizers, metrics...
Attending the hackathon is free and happens via the entry in the side menu.
The number of participants can be limited, depending on the tutoring and technical capabilities.
Upon registration, we will require:
- INFN Affiliation
- Research interests
- Proficiency level in Machine Learning and related tools
- Specific interests in Machine learning techniques
- The preferred use case for the third day (first and second choice)
For INFN employees
The event was approved by the INFN National Committee for Continuos Education (Commissione Nazionale Formazione). INFN Employees participating to the event can cover travel expenses with the funds labelded "Formazione" of their own INFN units.
INFN Employees taking part to the event are expected to notify the local responsible for Continuos Education of their unit to receive the mandatory endorsement from the unit's Director.
At the end of the event, the list of the partecipating INFN employees will be recorded in the national database of continuos education activities.
Please consider visiting again this page from time to time: we will add more resources for the hackathon and related projects as soon as they become available,.