Navigation

Lectures

About the course

This is the homepage of the decision support systems and machine learning course. The course has a weight of three ECTS and consists of lectures, workshops and exercise sessions.

The goal of the course is to introduce frameworks and algorithms for reasoning under uncertainty. The characteristics for this type of reasoning are that decisions have to be made on the basis of insufficient or uncertain information, and that the consequences of the decisions are usually subject to uncertainty. We shall consider various types of graphical models (e.g. Bayesian networks, decision trees, etc.) that can be used to represent such decision problems, and for which solution algorithms have been developed.
We shall also focus on how such systems may be constructed. In particular, we shall see how they can be learned automatically from databases and how a system can automatically be refined (based on new experience/data) once it has been put into use.

The course will consist of 15 lectures, and each lecture will be accompanied by an exercise session scheduled immediately before the next lecture.

The first lecture takes place Monday, September 4th at 12.30, and the first exercise session starts Tuesday, September 5th at 8.15.