Every winter semester
5 Credit points
For information on the duration of the modul, refer to the courses of study in which the module is used.
Non-official translation of the module descriptions. Only the German version is legally binding.
Students will have practical and basic theoretical knowledge of formal approaches and techniques for building technical systems that can act robustly and intelligently under uncertainty. In the tutorials accompanying the lecture students will learn how to apply and practice these approaches by working on small-scale practical projects.
The field of "Artificial Intelligence" is concerned with the design and realisation of information processing systems - "intelligent agents" - that are able to model cognitive performance and to exploit this for technical applications. Building on the basic knowledge acquired in the module 39-Inf-13 "Grundlagen künstlicher Kognition" or in the module "Artificial Intelligence", this module teaches more detailed and research-related aspects of how to construct intelligent agents that can behave robustly and intelligently under uncertainty. Starting from classical approaches to robust planning and searching, this includes modern probabilistic approaches to reasoning and decision-making like Bayesian belief networks or Markov Decision Processes.
A recommended prerequisite for this module is knowledge of knowledge representation and reasoning as can be acquired, for example, in the module "Artificial Intelligence".
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Remarks on Selection of Courses:
Either the tutorial or the project "Special topics of artificial intelligence" may be selected.
Module structure: 1 bPr 1
Portfolio of lecture accompanying practice exercises (pass mark being 50% of the points achievable). Final exam (written 60-90 min.; or oral 20-30 min.) about the contents of the lecture and tutorial.
| Degree programme | Profile | Recommended start 3 | Duration | Mandatory option 4 |
|---|---|---|---|---|
| Data Science / Master of Science [FsB vom 06.04.2018 mit Änderungen vom 01.07.2019, 02.03.2020, 21.03.2023 und 10.12.2024] | Variante 1 | 1. o. 3. | one semester | Compulsory optional subject |
| Data Science / Master of Science [FsB vom 06.04.2018 mit Änderungen vom 01.07.2019, 02.03.2020, 21.03.2023 und 10.12.2024] | Variante 2 | 1. o. 3. | one semester | Compulsory optional subject |
The system can perform an automatic check for completeness for this module.