Inhalt

[ 921CGELPRMU13 ] UE (*)Probabilistic Models

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(*) Leider ist diese Information in Deutsch nicht verfügbar.
Workload Ausbildungslevel Studienfachbereich VerantwortlicheR Semesterstunden Anbietende Uni
1,5 ECTS M1 - Master 1. Jahr Informatik Gerhard Widmer 1 SSt Johannes Kepler Universität Linz
Detailinformationen
Quellcurriculum Masterstudium Computer Science 2022W
Ziele (*)To provide an opportunity for students to experiment with probabilistic models and reasoning methods, in order to better understand the workings and limitations of these methods. This class is highly recommended as a supplementary course to the VO "Probabilistic Models", where the theoretical foundations are explained.
Lehrinhalte (*)Practical experiments with probabilistic models. Development of simple systems that model and reason about some given problem. Specific focus: (discrete) Bayes Nets and temporal models (Hidden Markov Models, Kalman Filter).
Beurteilungskriterien (*)Independent experimenting based on given problem specifications. Written and/or oral report on the results.
Abhaltungssprache Englisch
Lehrinhalte wechselnd? Nein
Präsenzlehrveranstaltung
Teilungsziffer 35
Zuteilungsverfahren Direktzuteilung