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