Stud.IP Uni Oldenburg
University of Oldenburg
23.10.2019 15:33:17
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
Faculty/Institute Department of Computing Science
Used in course of study
  • Master's Programme Business Informatics (Master) >
  • Master's Programme Computing Science (Master) >
  • Master's Programme Embedded Systems and Microrobotics (Master) >
  • Master's Programme Engineering of Socio-Technical Systems (Master) >
  • Master's Programme Engineering of Socio-Technical Systems (Master) >
Contact person
Module responsibility
Authorized examiners
Entry requirements
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
Reader's advisory
Recent eBooks, eTutorials
Links
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 AS (Akzentsetzung / Accentuation)
Modulart je nach Studiengang Pflicht oder Wahlpflicht
Lern-/Lehrform / Type of program 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
SWS 2.00
Frequency WiSe
Workload attendance 28 h