Resource-aware Machine Learning - International Summer School 2014

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.

Course leader

Bashir Al-Hashimi, Christian Bauckhage, Christian Bockermann, Jian-Jia Chen, Marlene Doert, Peter Marwedel, Katharina Morik, Melanie Schmiddt

Target group

PhD or advanced master students from computer science or related disciplines using machine learning as an application (e.g. astrophysics, biology, medicine, engineering)

Course aim

Topics of the lectures include: Data stream analysis. Energy efficiency for multi-core embedded processors. Factorising huge matrices for clustering. Using smartphones to detect astro particles.

Exercises help bringing the contents of the lecture to life. All participants get the chance to learn how to transform a smartphone into an extra-terrestial particle detector using machine learning.

Credits info

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.

Fee info

EUR 350: Early registration fee until 30th of June. Fee applies to every student attending the summer school.
EUR 400: Late registration fee.

Scholarships

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.