REGISTRATIONS WILL OPEN ON APRIL 18th!!!
Welcome to the first edition of the Machine Learning @ INFN (ML_INFN) starting level hackathon, dedicated to INFN Affiliates.
The course is to be considered as "starting level" for Machine Learning topics.The hackathon will be organized over 3 days, distributed as
- General introduction on ML and on its use in INFN (including Clouds)
- Tutored hands-on of specific use cases, attempting to reach fully working products; a review of the ML utilization in specific use cases of INFN interest
- The hackathon, with participants working in groups trying to achieve a goal in the form of a realistic analyses. In the latter part, presentation of their work is expected and discussed among all the groups.
The use cases for third day ("hackathon")
Upon registration, users will be asked to express a first and second preference 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):
- discrimination of hadronic jets from light and heavy flavours @ LHC
- autoencoders for VIRGO GW signal analysis
- segmentation of lesions in medical physics
Technical: access to INFN Cloud resources
Please read and comply with the recommendations in the side menu "technical prerequisite".
Machine learning related knowledge
The hackathon is to be considered as entry level for Machine Learning. Still, some fluency in the tools to be used is a prerequisite and it is needed to speed up the tutoring. In particular, we expect:
- some fluency in Python:
- basic program flow, definition and use of variables
- definition and utilization of functions
- package management (import, install via pip)
- some fluency in numpy:
- arrays, basic operations, reshaping
- some fluency on matplotlib:
- how to define and plot a basic figure
- some fluency on online python notebooks
- (like SWAN, Jupyter, Google Colab)
In case you do not feel proficient enough in the previous items, we suggest a list of entry level tutorial / documentation we advise to read and test before the Hackathon, in order to be able to follow with profit the presentations and the hands-on. This can be found in the side menu, at the entry "Pre-tutorial Self-teaching".
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 info like
- 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)
Please consider visiting again this page from time to time; as soon as available, we will add more resources for the hackathon and related projects.