Dortmund, Germany

Resource-aware Machine Learning

when 4 September 2012 - 7 September 2012
duration 1 week
fee EUR 350

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

João Gama, Sangkyun Lee, Jan Madsen, Peter Marwedel, Nico Piatkowski, Jörg Rahnenführer, Wolfgang Rhode, Tim Ruhe

Target group

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

Course aim

Topics of the lectures include: Mining of ubiquitous data streams, criteria for efficient model selection or dealing with energy constraints... The theoretical lessons are accompanied by exercises and practical introductions: Analysis with RapidMiner and R, massively parallel programming with CUDA.rnA Data Mining Competition lets you test your machine learning skills on real world smartphone data.

Fee info

EUR 350: Fee applies to every student attending the summer school.


Student grants covering registration, travel and accommodations 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.