This course provides an overview of the state-of-the-art in the areas of pattern recognition and probabilistic inference, and a thorough understanding of the most successful methods in those areas.
After successful completion of the course, the students are familiar with stragtegies to solve conceptual and theoretical problems in machine learning. They have the ability to apply state-of-the-art algorithms to real-world problems and are familiar with the advantages and disadvantages of different algorithms.
- Inference from uncertain facts (Bayesian networks etc)
- Kernel methods
- Mixtures of experts