Stud.IP Uni Oldenburg
University of Oldenburg
23.10.2019 16:28:28
inf534 - Probabilistic Modelling II (Complete module description)
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Module label Probabilistic Modelling II
Module code inf534
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) >
Contact person
Module responsibility
Authorized examiners
Entry requirements
Skills to be acquired in this module
Probabilistic 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. In this part of the seminar we read, present, and discuss recent research papers.

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


Methodological competence
The students:
  • acquire advanced skills in the design, implementation, and identification of probabilistic models with Bayesian methods
  • acquire knowledge about alternative 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 publications
Links
Language of instruction German
Duration (semesters) 1 Semester
Module frequency halbjährlich
Module capacity unlimited
Reference text
Associated wiht the module:
  • inf533 Probabilistische Modellierung I
Modullevel AS (Akzentsetzung / Accentuation)
Modulart je nach Studiengang Pflicht oder Wahlpflicht
Lern-/Lehrform / Type of program
Vorkenntnisse / Previous knowledge - Grundkenntnisse Progeammierung
Examination Time of examination Type of examination
Final exam of module
seminar talk, reflective written summary
Course type Seminar
SWS 2.00
Frequency SuSe
Workload attendance 28 h