Partner course
Computational Learning Theory and Beyond
Start date
3 weeks


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.

Which topics will be covered?

  • 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 

What will I achieve?

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.  


Which prerequisites do I need to fulfill?

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

Who is offering this course?

Karen Seidel

The creators of the learning opportunities are responsible for their content.

What else do I need to know?

Learning format:
Online course
Record of Achievement