From September 23 to 25, 2019, the Training School on Machine Learning for Communications takes place in Paris. The event is part of the IEEE Communications Society's Machine Learning for Communications Emerging Technologies Initiative.

Within the last few years, research on Machine Learning for communications has demonstrated its potential to significantly improve different aspects of communication systems. In the field of wireless communication, the processing of emerging Beyond 5G technologies entails complex optimization problems, problematic modeling and low complexity requirements. Machine Learning methods and data-driven algorithms have recently proposed new approaches of modeling, designing, optimizing and implementing communication systems.

To present the latest advances in the field of Machine Learning for communications and to further prospect this emerging research field, the Machine Learning Training School takes place. Lectures from internationally renowned researchers from academia and industry will be given during this three-day training school.

Speakers:

• Vincent Poor (Princeton University)
• Stephane Mallat (ENS Paris) – to be confirmed
• Jakob Hoydis (Nokia Bell labs)
• Merouane Debbah (Huawei)
• Renato Cavalcante (TU Berlin)
• Slawomir Stanczak (TU Berlin)
• Mehdi Bennis (University of Oulu)
• Christophe Moy (CentraleSupélec)
• Lilian Besson (CentraleSupélec)
• Emil Matus (TU Dresden)

Ph.D. students and early-stage researchers in the wide field of communications are particularly encouraged to attend. Background in the Machine Learning field is not mandatory.

Further information about the event can be found here .