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Into DeeP

Reference number
Coordinator RISE Research Institutes of Sweden AB - RISE SICS Västerås AB
Funding from Vinnova SEK 1 000 000
Project duration April 2018 - December 2018
Status Completed
Venture Kunskapssatsningar för industrins digitalisering
Call Kunskapsspridning för ökad digitalisering i industrin

Purpose and goal

The aim of the project is to contribute to technical transfer and increased competence which derives from the larger project DEEP, Deep Process Learning. The objective is to increase the knowledge level in industry within the areas artificial intelligence (AI), machine learning (ML) and deep learning (DL) through web-based training which is publicly available (OER, Open Educational Resources). The project has developed and launched four web-based training modules which are available on the website AIClass.se

Expected results and effects

The project has developed and launched four web-based training modules which are available on the website AIClass.se which will be administrated and further developed by Mälardalen University after the project. One expected effect is that the result of the project, the training modules, contributes to an increased understanding of AI, ML and DL in Swedish companies. A desired future effect is that the increased knowledge about the mentioned technologies makes investments more effective. Another expected effect is that this project leads to further collaboration within the area.

Planned approach and implementation

The result of the project, the web-based training modules, have been developed mainly by Mälardalens University and RISE SICS Västerås. The companies involved have provided industrial requirements and have also verified the material during the project. The project has been carried out in close collaboration with the larger DEEP project, Deep Process Learning (management, meetings etc).

External links

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 20 December 2018

Reference number 2018-00524

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