Stud.IP Uni Oldenburg
University of Oldenburg
02.04.2023 05:03:13
inf533 - Probabilistic Modelling I (Complete module description)
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Module label Probabilistic Modelling I
Modulkürzel inf533
Credit points 3.0 KP
Workload 90 h
Institute directory Department of Computing Science
Verwendbarkeit des Moduls
  • Master's Programme Business Informatics (Master) > Akzentsetzungsmodule der Informatik
  • Master's Programme Computing Science (Master) > Angewandte Informatik
  • Master's Programme Engineering of Socio-Technical Systems (Master) > Embedded Brain Computer Interaction
  • Master's Programme Engineering of Socio-Technical Systems (Master) > Systems Engineering
Zuständige Personen
Fatikow, Sergej (Module responsibility)
Marx Gomez, Jorge (Module responsibility)
Lehrenden, Die im Modul (Prüfungsberechtigt)
Prerequisites
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.


Self-competence
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
Literaturempfehlungen
Recent eBooks, eTutorials
Links
http://www.uni-oldenburg.de/en/computingscience/lcs/probabilistic-programming/
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 Prüfungszeiten Type of examination
Final exam of module
Will be announced in the lecture
Presentation, reflective summary
Form of instruction Seminar
SWS 2
Frequency WiSe
Workload Präsenzzeit 28 h