Chair of Artificial Intelligence and Machine Learning

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Preference Learning and Ranking (SS 2024)

The lecture will give a general introduction to methods for modeling and learning preferences, with a specific focus on methods for ranking. The topic of preferences has recently attracted considerable attention in artificial intelligence (AI) research, notably in fields such as autonomous agents, constraint satisfaction, planning, and information retrieval. Preferences provide a means for specifying desires in a declarative way, which is a point of critical importance for AI. Drawing on past research on knowledge representation and reasoning, AI offers qualitative and symbolic methods for treating preferences that can reasonably complement hitherto existing approaches from other fields, such as decision theory. Needless to say, however, the acquisition of preference information is not always an easy task. Therefore, not only are modeling languages and suitable representation formalisms needed, but also methods for the automatic learning, discovery, modeling, and adaptation of preferences.


  • Mathematical fundamentals (linear algebra, calculus)
  • Algorith fundamentals (algorithms and data structures)
  • Probability and statistics fundamentals
  • Programming


General Information


Eyke Hüllermeier



Time and place:

Lecture Tuesday 16-18
Exercises 1 Thursday 14-16
Exercises 2 Friday 10-12


2 SWH Lecture +2 SWH Exercises

Target audience:

  • Informatics
  • Mathematics
  • Statistics
  • Data science


  • Timo Kaufmann
  • Tobias Oberkofler