Inhalt

[ 921COENPRMV13 ] VL Probabilistic Models

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Workload Ausbildungslevel Studienfachbereich VerantwortlicheR Semesterstunden Anbietende Uni
3 ECTS M1 - Master 1. Jahr Informatik Gerhard Widmer 2 SSt Johannes Kepler Universität Linz
Detailinformationen
Quellcurriculum Masterstudium Computer Science 2021S
Ziele (*)After this class, students will know and understand the basic concepts of a large and centrally important class of methods in modern Artificial Intelligence: Probabilistic Graphical Models, used for representing and reasoning about uncertain information and knowledge in complex real-world scenarios. All three aspects related to such models will be covered: model semantics, inference, and learning. Both basic concepts and specific algorithms will be taught, and all methods will be derived in a mathematically rigorous way. Students will understand how such models can be used for problem modeling and solving, and what their limitations are.
Lehrinhalte (*)Elementary Concepts: Probability Distributions, Density Functions, (conditional) independence, Probabilistic Reasoning and Inference. Bayesian Networks: Representation, Semantics, Factorisation. Inference in Bayesian Networks: Exact Inference; Approximate Inference: Stochastic Sampling, Markov Chain Monte Carlo (MCMC) Methods. Learning Bayesian Networks: Parameter Learning (maximum likelihood and Bayesian estimation), Structure Learning, Generative vs. Discriminative Models. Special Types of Bayes Nets: Linear Gaussian Network Models. Modelling and Predicting Temporal Processes: Dynamic Bayes Networks, Hidden Markov Models, Kalman Filters; Particle Filters. Selected Applications of Probabilistic Graphical Models.
Beurteilungskriterien (*)Written exam at the end of the semester.
Lehrmethoden (*)Lecture series with written materials (presentation slides) provided regularly in electronic form.
Abhaltungssprache Englisch
Literatur .
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