Machine learning is the key technology to discover information and concepts hidden in huge amounts of data. At the same time, availability of data is ever increasing. Better sensors deliver more accurate and fine-grained data, more sensors a more complete view of the scenario. While this should lead to better learning results, it comes at a cost: Resources for the learning task are limited, restricted by computational power, communication restrictions or energy constraints. The increased complexity needs a new class of algorithms respecting the constraints.
From to , the TU Dortmund University, Germany, will host a summer school addressing solutions to these constraints.
For the summer school, world leading researchers in machine learning and embedded systems are giving lectures on several techniques dealing with huge amounts of data, distributed data and constraints of embedded systems.
The summer school is open for international PhD or advanced master students, who want to learn cutting edge techniques for machine learning with constrained resources. Available slots for participants are strictly limited, so it is essential to register early. Excellent students may apply for a student grant to fund travel and accommodation.
The international summer school on resource-aware machine learning is part of the graduate school of the Collaborative Research Center SFB 876. The research center targets both ends of the machine learning spectrum: Small devices, with high restrictions due to the device, and high-dimensional data with a complexity exceeding even large computing center's capacity.