Institute of Photogrammetry and GeoInformation Research Research Groups Earth Observation
Interdisciplinary Center for Applied Machine Learning - ICAML (2019)

Interdisciplinary Center for Applied Machine Learning - ICAML (2019)

Team:  D. Wittich, F. Rottensteiner
Year:  2017
Funding:  Bundesministerium für Bildung und Forschung
Duration:  11/2017-11/2019
Is Finished:  yes
Further information https://www.icaml.org

In cooperation with: Institut für Kartographie und Geoinformatik (IKG) 

Idea and aim:

The ICAML (Interdisciplinary Center for Applied Machine Learning) aims at increasing the accessibility of machine learning across disciplines. Therefore, three fundamental components are developed and used.

The first component consists of offering courses and events. Here we emphasize on modules which are accessible on demand via the Internet. These modules are supplemented with presentations and lectures. The range of contents includes basic tutorials, methods of machine learning as well as the handling of specific data types.

The second component is the provision of information and knowledge. Therefore the existing knowledge of the incorporated persons and institutes is gathered and edited to fit the platforms concept. In addition external sources are gathered, commented and assessed with respect to the contents. These packages of knowledge not only support the courses but also act as overviews for anyone who is interested in machine learning. The collection of these packages of knowledge is not static, but will grow and being updated with the gained experiences.

The third component is the supply, administration and maintenance of a computation cluster. This cluster provides the computational power, which is indispensable for the participants in order to gain practical experience. On the one hand different projects and theses in the field of deep learning are made possible. On the other hand the cluster plays an essential role for the practical labs, which go along with the offered courses.

If you are interested in machine learning, or want to use our services for a project or thesis visit www.icaml.org.