Applied Machine Learning
The Applied Machine Learning group aims to develop and apply novel Machine Learning methods for real-world applications. Emerging digitalization allows companies from different fields of industry to produce and collect data from various resources. This is realized by technologies like the Internet of Things (IoT), cyber-physical systems and cloud-computing. To effectively handle this big data, Deep Learning and Signal Processing techniques are needed to provide powerful and promising solutions.
Group Head
Group Members
Current Students
If you are interested in writing a Bachelor’s or Master’s thesis in our group, please check the lab’s Student Theses and Jobs.
Projects
- Applications of Deep Learning for Signal Analysis
- Development of neural networks and machine learning algorithms for online handwriting recognition
- Tracking in the Deutsche Museum Nürnberg
- Machine Learning For Predictive Analytics
- Holistic customer-oriented service optimization for fleet availability
- Tool Tracking
- Digital Twin of Rheuma
- Machine Learning for CT-Detector Production
- VERA (Video-based Re-Identification for Animals)
- TegrityHub
- Multimodal Machine Learning for Decision Support Systems
- Biologically-inspired self-supervised systems
- Automated German Crossword Resolution
- TEF-Health
