Mediterranean Machine Learning summer school
Support the ML community in Mediterranean countries, and encourage diversity in ML
[Nov 10th] Tentative schedule available on Schedule.
[Nov 11] School slack workspace shared with registered students, this will be our main communication channel going forward.
[Nov 12] If you are successfully registered but haven't received the communication please check the Spam folder of the e-mail you used to register.
11th - 16th of January 2021 (in virtual form)
31st of August - 5th of September 2020 (postponed)
The Mediterranean Machine Learning (M2L) summer school will be structured around 6 days of keynotes, lectures and practical sessions. The program will include social or cultural activities to foster networking. Participants will be encouraged to (optionally) present their work at evening poster sessions during the school and to interact with our sponsors and with each other during the coffee breaks throughout the week.
Lectures and laboratories taught by local and international AI experts. State-of-the-art content and code will be accessible to all school participants.
Format and dates:
The next edition of the Mediterranean Machine Learning (M2L) summer school will take place in virtual form, join us for a week of lectures from the 11th to the 16th of January 2021.
The target audience will consist primarily of Master and Doctoral students, academics, and practitioners from all around the world, with a focus on the Mediterranean area. The school will be advertised with a public call and participants will be selected on the basis of merit and to promote diversity. We aim to have attendees from higher education, with technical background and some understanding and practical experience of machine learning.
Natural Language Processing
(Deep) reinforcement learning
Bayesian and causal inference
Applied deep learning (E.g., healthcare, physics, weather)
....and many more!
Promote diversity and inclusion: AI is revolutionizing several fields, from computer vision to autonomous driving and robotics. Its potential to radically change our society calls for this technology to be mastered and shaped by everyone, with equal representation of genders, ethnicities, nationalities, religions and economic backgrounds.
Boost networking and knowledge transfer: M2L brings together early-stage researchers, practitioners and world-leading experts, providing access to high quality education, and strengthening the local and foreign machine learning ecosystem by fostering durable connections among researchers, as well as among practitioners and industry.
Support dialogue and collaboration: the school aims to promote an informed dialogue among companies, academia and institutions, encouraging them to take an active role in shaping, regulating and supporting the evolution of the artificial intelligence field, as well as to facilitate the exchange of ideas and the establishment of partnerships.