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measurement and interpretation and potential usefulness in human-machine interfaces. They should learn to analyze and formally describe the problems, challenges and potential of using these signals for
Section: FB3
AI in video games, this thesis contributes to the fields of game user research, game AI, machine learning and player modeling within both academia and industry and illustrates significant advances in the
software projects. The mathematical focal points are * Inverse Problems*(data analysis and machine learning, sparsity, non-convex regularization), *Optimization*(optimal control and regularization, continuous
computer systems and robots that have the abilities of humans and other biological creatures, e.g. learning new facts, inferring with uncertain knowledge, understanding texts and language, visually capturing
recognized, human experts instruct and continuously improve the CNN to be developed using supervised learning.
health environments in complex multi-device scenarios. This includes the application of machine learning (ML) techniques for data-driven long-term evaluation and prediction. This will enable users to play
218-64424 ORCID Research Interests Knowledge representation and ontologies Formal semantics Machine learning models in Natural Language Processing Human Computer Interaction (HCI) Possible thesis projects:
combination of different HCI methods. Furthermore, these methods shall be founded by legal as well as learning and motivational psychological viewpoints. Within the frameworks of this project, a simulation of
narratives in social media texts Evaluating (deep) language understanding: e.g. in comparing machine learning/symbolic AI/hybrid AI Visualization for NLU: e.g. interactive topic networks or narrative graphs
sports, feedback systems, and motion analysis in weight training. His focus is on applying machine learning methods to motion data recorded using different sensors. He studied computer science at the University