Stud.IP Uni Oldenburg
University of Oldenburg
29.11.2021 04:00:51
inf533 - Probabilistic Modelling I (Complete module description)
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Module label Probabilistic Modelling I
Module code inf533
Credit points 3.0 KP
Workload 90 h
Institute directory Department of Computing Science
Applicability of the module
  • Master's Programme Business Informatics (Master) > Akzentsetzungsmodule der Informatik
  • Master's Programme Computing Science (Master) > Angewandte Informatik
  • Master's Programme Embedded Systems and Microrobotics (Master) > Akzentsetzungsmodule
  • Master's Programme Engineering of Socio-Technical Systems (Master) > Embedded Brain Computer Interaction
  • Master's Programme Engineering of Socio-Technical Systems (Master) > Systems Engineering
Responsible persons
Möbus, Claus (Authorized examiners)
Lehrenden, Die im Modul (Authorized examiners)
Skills to be acquired in this module
Probabilistic Bayesian models are generated with special tools (e.g. BUGS, JAGS, STAN) or domain specific programming languages (, WebPPL, PyMC3, …etc.). If they mimic cognitive processes of humans (e.g. pilots, drivers) or animals they could be used as cooperative assistance systems in technical or financial systems like cars,robots, or recommenders.

Professional competence
The students:
  • learn to map problem to model classes to come up with practical solutions

Methodological competence
The students:
  • acquire basic skills in the design, implementation, and identification of probabilistic models with Bayesian methods
  • acquire knowledge about alternative non-Bayesian machine learning methods

Social competence
The students:
  • learn to present and discuss probabilistic theories, methods, and models.

The students:
  • reflect and evaluate chances and limitations of probabilistic approaches
  • learn to deliberate on machine-learning alternatives
Module contents
Theories, methods, and examples of Bayesian models with practical applications
Reader's advisory
Recent eBooks, eTutorials
Languages of instruction German, English
Duration (semesters) 1 Semester
Module frequency jährlich
Module capacity unlimited
Reference text
Associated with the module:
  • inf534 Probabilistic Modelling II
Modullevel / module level AS (Akzentsetzung / Accentuation)
Modulart / typ of module je nach Studiengang Pflicht oder Wahlpflicht
Lehr-/Lernform / Teaching/Learning method S
Vorkenntnisse / Previous knowledge Programmierkenntnisse
Examination Time of examination Type of examination
Final exam of module
Will be announced in the lecture
Presentation, reflective summary
Course type Seminar
Frequency WiSe
Workload attendance 28 h