From August 19-23, 2019, the two Germany based Big Data Competence Centers ScaDS Dresden/Leipzig and BBDC organized the 5th International Summer School on Big Data and Machine Learning. We continued our previous efforts to establish this summer school series and 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 took place at Technical University (TU) in Dresden.
Program & Highlights
The Summer School bridged the gap between the research fields Big Data and machine learning, with contributions from many internationally well-known experts from various fields. The highly recognized 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 topics 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. Almost sixty participants could not just take part and connect to the expert, but could also contribute a poster about own research activity in a poster session and during the whole week to trigger discussions between participants. As social activity an archery tournament brought fun and a contrast into the program as well as triggered some 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 can find the full program with additional information on the speakers and their topics: