Prof. Dr. Felix Weitkämper

We will be covering semantic foundations, inference and learning for Bayesian networks, functional causal models and rule-based approaches to causal and probabilistic reasoning. This will equip you with the ability to model systems governed by complex, probabilistic relationships and reason both within the system and on interventions into the system. We will discuss the types of knowledge required to answer certain queries and how that is reflected in the choice of model. Lastly, we will see to what extent access to data can assist us in constructing models that can give the answers we need in a principled way.

Decision-making is empowered by (big) data through the use of machine learning and data analytics principles.

This course introduces students to software development using Python. It covers basic coding, object-oriented programming, Django, and key practices like agile methods, version control, and clean code.

This module is an introduction to logic and symbolic AI. We introduce the principles of computational logic and the fundamentals of the logic programming paradigm as exemplified by the Prolog language. We then use these concepts to solve classical artificial intelligence tasks such as problem solving and game playing.

Shaping the future with intelligence!