From August 19-23, 2019, the two Big Data Competence Centers in Germany, ScaDS Dresden/Leipzig and BBDC, organized the 5th International Summer School on Big Data and Machine Learning. With this event, we continued our previous efforts to establish this summer school series. Furthermore, we broadened the topic spectrum covering state-of-the-art techniques in Big Data processing and analytics as well as novel methods in Machine Learning and Artificial Intelligence.
The Summer School 2019 took place at Technical University (TU) in Dresden.
Program & Highlights
The Summer School 2019 bridged the gap between the research fields Big Data and Machine Learning, with many contributions from internationally renowned experts from various fields. The top-flight program included key notes from IBM, NVIDIA, Intel, and speakers from academia of both competence centers BBDC and ScaDS Dresden/Leipzig as well as invited speakers.
The lectures span a wide range of topics around large scale and data intensive computing (Big Data) and exciting new trends in Machine Learning, such as uncertainty quantification, distributed Machine Learning and architectural optimization for Deep Learning. The event was attended by nearly sixty participants, who could not just connect with the experts, but also submit a poster on their own research activity. The poster sessions, which lasted throughout the week, stimulated discussions between the participants. As social activity, an archery tournament brought fun and a contrast into the program and sparked some friendly competition among the participants.
Prior to the Summer School, there was a hackathon for hands-on development of an application in the Machine Learning area.
The topics of the summer school cover a broad range of up-to-date topics:
- Big Data and HPC
- Machine learning techniques
- Scalable graph analytics
- Data processing and streaming
- Machine learning and data analytics for applications
- Large scale visual analytics
- Web-scale information extraction
Below you find the full program with additional information on the speakers and their topics: