28 September 2017
Resource-aware Machine Learning - International Summer School 2017
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.
Rakesh Agrawal, Sebastian Buschjäger, Kristian Kersting, Thomas Liebig, Wayne Luk, Mojtaba Masoudinejad, Rob Maunder, Katharina Morik, Nico Piatkowski, Chris Schwiegelshohn, Olaf Spinczyk
PhD or advanced master students from computer science or related disciplines using machine learning as an application (e.g. astrophysics, biology, medicine, engineering)
Topics of the lectures include: Machine learning on FPGAs, Deep Learning, Probabilistic Graphical Models and Ultra Low Power Learning.
Exercises help bringing the contents of the lectures to life. The PhyNode low power computation platform was developed at the collaborative research center SFB 876. It enables sensing and machine learning for transport and logistic scenarios. These devices provide the background for hands-on experiments with the nodes in the freshly built logistics test lab. Solve prediction tasks under very constrained resources and balance accuracy versus energy.
While we do not officially provide credits for the summer school, you will get a receipt of attendance including the topics of the course. You can use this at your home institute to apply for credits.
EUR 350: Early registration fee until 30th of June. Fee applies to every student attending the summer school.
EUR 400: Late registration fee.
Student grants covering travel and accommodation up to 500,- € will be sponsored. A committee will select up to five of the best students. The criteria are the quality of the student and the distribution of student grants over the world. More information