After successful completion of the course, students are able to delineate the main application areas, formalisms, and methodologies for probabilistic modeling and reasoning in artificial intelligence.
The course gives an overview on the area of probabilistic modeling and reasoning in artificial intelligence. Some planned topics are summarized as follows: basics of probability theory; Bayesian networks; probabilistic logic; nonmonotonic probabilistic inference; probabilistic logic programming; decision theory; planning under uncertainty in Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs); game theory.
The course will be given in English.
If necessary, the dates/times of the lectures will be adapted to the availability of the attendants, so registration is required during the given registration period.
ECTS Breakdown
18.0 h preparation at home
18.0 h lectures/exercises in the lecture hall
18.0 h exercise problems
20.0 h preparation for oral exams
1.0 h oral exams
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75.0 h = 3 ECTS
