Perception: Natural Language Processing and Computer Vision

Organisation

• Lecture (Prof. Dr. Ralf Möller)
• Seminar (Malte Luttermann, M.Sc.)

Content

• Stochastic fundamentals: Markov networks, Markov random fields, dynamic Bayesian networks, query and query
  response algorithms, sampling methods, learning methods with incomplete data (expectation-maximization: EM, Baum-Welch method), PAC learning principle
• Probabilistic language models, topic models, latent Dirichlet allocation (LDA), thematic developments over time represented with dynamic Bayesian networks
• Transformation networks as probabilistic models, training methods for convolutional and transformation networks, application-specific training through fine-tuning (deep and shallow), integration of special (symbolic) problem solvers
  in GPTs, differential programming
• Analysis of videos: Object detection with transformation network architectures (YOLO)
• Probabilistic computational networks (e.g. with applications in image processing), query answering and scalability, transformation of probabilistic models to probabilistic computational networks, control of large language models with probabilistic models