SS 06 - Challenges of Machine Learning in Intelligent Technical Systems

Special Session Organized by

Diana Göhringer, Technische Universität Dresden, Germany and Christoph-Alexander Holst, inIT – Institute Industrial IT, Germany and Alexander Maier, Bielefeld University of Applied Sciences, Germany and Anton Pfeifer, inIT – Institute Industrial IT, Germany

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Machine Learning techniques have achieved outstanding performances in numerous computing problems. But the requirements of technical and industrial systems still pose considerable challenges for data-driven machine learning methods. Challenges originate from the imperfectness of data in technical systems resulting in uncertainties, imprecision, conflict, incompleteness, low quality, and sparsity. On the other hand, technical and industrial systems require learners and models which are able to run in real-time, on limited hardware, are interpretable, robust, highly accurate, adaptable, and secure. This special session aims at advancing and enhancing machine learning methods regarding these challenges and requirements. Applications range from, but are not limited to, predictive maintenance and analytics, quality management, product design, assistance systems, optimization, and computer vision.

Topics under this session include (but not limited to)

  • Explainable Machine Learning Models
  • Robust Machine Learning in Light of Uncertain and Error-prone Data
  • Adapting Models in Non-stationary Environments
  • Learning on Streaming Data
  • Machine Learning on Resource-limited Hardware
  • Predictive Maintenance and Remaining Useful Life
  • Information Fusion
  • Cognitive Computing
  • Reinforcement Learning in Industrial Environments