inf534 - Probabilistic Modelling II (Course overview)

inf534 - Probabilistic Modelling II (Course overview)

Department of Computing Science 3 KP
Semester courses
Form of instruction: Seminar
  • No access 2.01.534 - Show lecturers
    • Prof. Dr. Claus Möbus

    Friday: 10:00 - 12:00, weekly (from 16/04/21), SE
Hinweise zum Modul
Reference text
Associated wiht the module:
  • inf533 Probabilistische Modellierung I
Module examination
seminar talk, reflective written summary
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