The ML2R invites you to the International Summer School
30. July 2020
This year’s collaborative Summer School will provide you with an insight into the latest research in Machine Learning, with a twist on resource-awareness of methods and algorithms. The online event, taking place between August 31 and September 4, 2020, will feature a mixture of pre-recorded and live sessions, including a dedicated space for PhD and PostDoc scientists to present their research and a hackathon featuring real-world Machine Learning tasks.
Together with the Collaborative Research Center 876 “Providing Information by Resource-Constrained Data Analysis” and the Artificial Intelligence Group at TU Dortmund University, the Competence Center Machine Learning Rhine-Ruhr will host an international Summer School. The free online event will take place between August 31 and September 4, 2020.
The Summer School will bring together experts from the research fields of Data Analysis (Machine Learning, Data Mining, Statistics) and Embedded Systems (Cyber-Physical Systems). In their lectures, they will address the resource limitations of devices in the context of Machine Learning and data analysis. The Summer School will also give participants the opportunity to present their research and network with each other.
The Summer School will be accompanied by a hackathon in the form of a Kaggle Challenge, in which participants can test their knowledge of Machine Learning and Cyber-Physical Systems. In a warehouse scenario, participants will make position predictions for robots, using sensor data. The winners of the challenge will then have the opportunity to control the robots used to transport goods in a live session. Moreover, they will be invited for further research cooperation to Dortmund.
The event is aimed at doctoral students and scientists, as well as practitioners from industry. Further information about the Summer School and registration, which is now open, can be found at: https://www-ai.cs.tu-dortmund.de/summer-school-2020/
Technische Universität Dortmund
Otto-Hahn-Str. 12 / R4.022