Inhalt

[ 921CGELPRMU13 ] UE Probabilistic Models

Versionsauswahl
Workload Education level Study areas Responsible person Hours per week Coordinating university
1,5 ECTS M1 - Master's programme 1. year Computer Science Gerhard Widmer 1 hpw Johannes Kepler University Linz
Detailed information
Original study plan Master's programme Computer Science 2025W
Learning Outcomes
Competences
See lecture series (Vorlesung) by the same name.
Skills Knowledge
See lecture series (Vorlesung) by the same name. See lecture series (Vorlesung) by the same name.
Criteria for evaluation Implementation of, and experimentation with, simple algorithms, based on given problem specifications; written and/or oral report on the results.
Methods Students implement simple inference and learning algorithms related to probabilistic graphical models; they perform systematic experiments with toy problems and datasets, in order to deepen their understanding of probabilistic modeling and reasoning. Selected students report on their results and experiences in class.
Language English
Study material The lecture slides of the corresponding lecture series course (VO).
Changing subject? No
Further information This exercise course (UE) and the corresponding lecture series course (VO) form a didactic unit. The study results described here are achieved through the combination of these two courses.
On-site course
Maximum number of participants 35
Assignment procedure Direct assignment