In this T-shaped course you will be introduced to computational learning theory and get a glimpse of other research towards a theory of artificial intelligence. "T-shaped" means that on the one hand we will concentrate on different learning models in depth, on the other hand we want to give a broad overview and invite experts from other AI projects to show what else can be done in AI.

• Introduction to computational learning theory  

• Research towards a theory of artificial intelligence.  

• A hands-on binary classification task using a support vector machines 

• Strategies that work not only for one specific classification task but more universally for a pre-specified set of such.  

• Different learning models which are all based on a modular design 

• Digestible insights into other approaches towards a theory of AI 

  • Stable matchings, evolutionary algorithms, fair clustering, game theory, low-dimensional embeddings, submodular optimization and 3-satisfiability 

By the end of the course, you‘ll be able to... 

• understand the basics of computational learning theory.  

• classify the elements of a given set into two groups (predicting which group each one belongs to) on the basis of given labeled data. 

• choose an appropriate learning model.  

• Familiarity with mathematical notation (basic studies at the university)