Intelligent systems are characterized by learning processes that allow the acquisition of new skills and deal with a large amount of heterogeneous, uncertain and probabilistic data. Due to the complexity of the real-world situations represented by them, the automatic abstraction of information from data, the formation of appropriate representative models, and the semantic processing of existing information is essential to build intelligent systems for digital transformation, such as mobility To enable industry, medicine and education.
Mitglieder
Projekte
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AutoML MOOC Online Course at AI-Campus.org about Automated Machine Learning
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BacData Building an analytics framework for precision microbiology to fight biofilm-associated infections
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BIAS Bias and Discrimination in Big Data and Algorithmic Processing. Philosophical Assessments, Legal Dimensions, and Technical Solutions
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Big Data for Cochlea Implants Understanding Cochlear Implant Outcome Variability using Big Data and Machine Learning Approaches |
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BigMedilytics Application of Big data technologies for supporting more efficient and effective healthcare systems |
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BOOST4.0 - Big Data for Factories Big Data Value Spaces for COmpetitiveness of European COnnected Smart FacTories 4.0
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CHORUS A highly optimized hardware/software module library for intelligent sensor systems in highly automated driver assistance applications based on the reconfigurable Dream Chip Technologies DCT10A SoM platform
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Cleopatra Cross-lingual Event-centric Open Analytics Research Academy
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Coding of Sequencing Data New coding methods allow sequencing data to be streamed
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Cognitive Economy Intelligence Plattform für die Resilienz wirtschaftlicher Ökosysteme Semantic data and AI analysis tools for the management of economic crises from the perspective of business, administration, politics, and research.
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