Objectives and outcomes
The objective is to present the basics of decision theory and related areas, discuss the current situation
in the field of decision support systems and provide examples of decision support systems. Students
creatively contribute to the development of decision support systems in their domain of application,
using, improving and synthesising quantitative methods and available technological solutions.
Lectures
Introduction to decision theory. Decision-making process. Aspects of probability and statistics in the
decision theory. The aspect of the fuzzy logic in the decision theory. Decision tree technique.
Introduction to operational research. Queues. Optimisation. Linear programming. Group decision
making. The Delphi technique. Brainstorming technique. Multi-criteria decision making models. Technologies
for the development of decision support systems. Data storage. Dimensional modelling and dimensional
access to data. Business intelligence and decision informatics. Online analytical data processing. Ad hoc
inquiries. Key performance parameters. Knowledge discovery. Application of
artificial intelligence in decision support systems. Expert systems. Intelligent agents. Data rules detection
algorithms. Logistic regression.
Practical classes
Origin and development of decision support systems. Critical market review of decision support systems.
Design and implementation of decision support systems. The contents covered in lectures are applied to
solving tasks and specific problems. Students are introduced to selected open-source tools.