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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Alexandria The ALEXANDRIA project (ERC Nr. 339233) aims to develop models, tools and techniques necessary to explore and analyze Web archives in a meaningful way |
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AMA - Applied Machine Learning Academy The aim of the AMA project is s to build up the Applied Machine Learning Academy (AMA) based on a sustainable, flexible and applied qualification concept |
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AutoPIN Machine learning and signal processing for automatic analysis of sensor data |
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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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BigMedilytics Application of Big data technologies for supporting more efficient and effective healthcare systems |
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BLINKER - Localization and Navigation With a 360°-Camera The BLINKER project is a collaboration between L3S and Goetting KG, a company which is specialized on driverless transportation systems |
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BOOST4.0 |
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BRENDA II BRaunschweig ENzyme DAtabase ist das weltweit größte Informationssystem für biochemische, molekularbiologische und funktionale Enzymdaten |
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CampaNeo Platform for real-time vehicle data campaigns |
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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 |