B-KUL-H02D2A Uncertainty Reasoning in Knowledge Systems
General information
Taught by
De Raedt Luc
(coordinator)
Bruyninckx Herman
De Laet Tinne
De Raedt Luc
Aims
The student understands and appreciates the role and need for uncertainty in artificial intelligence systems.
The student knows, understands and is able to apply the Bayesian approach for dealing with uncertainty; he is familiar with the key concepts and algorithms underlying graphical models (such as Bayesian networks (directed graphical models), Markov networks (Markov random field, undirected graphical model), Factor graphs, and Hidden Markov models) such as modeling, inference and learning.
The student understands how techniques for reasoning about uncertainty can be integrated with logic for reasoning and learning, that is, he is familiar with statistical relational learning principles and techniques.
The student is able to identify the common, generic concepts and algorithms in papers published in various domains, using different terminology and having different trade-offs in their knowledge systems.
Previous knowledge
Default requirements for all Master of Artificial Intelligence students.
Content
The course will be based on (selected) parts of David Barber's forthcoming book on Bayesian Reasoning and Machine Learning, available from http://www.cs.ucl.ac.uk/staff/d.barber/brml.
This course is included in
Master of Science in Artificial Intelligence
(Option Engineering and Computer Science (ECS)) (Required)
(Option Cognitive Science (CS))
Master of Science in de ingenieurswetenschappen: biomedische technologie
Master of Science in de ingenieurswetenschappen: computerwetenschappen (geen nieuwe inschrijvingen in 2011-2012)
(Artificiële intelligentie)
Master of Science in Artificial Intelligence
(Option Speech and Language Technology (SLT))
Master of Science in de ingenieurswetenschappen: computerwetenschappen (nieuw programma, start in 2010)
(Hoofdspecialisatie Artificiële intelligentie)
Master of Science in Biomedical Engineering
Master of Science in de toegepaste informatica
(Artificiële intelligentie en gegevensbanken)
Master of Science in de informatica (uitdovend, enkel 2e fase)
(Specialisatie artificiële intelligentie)
Course Material
Text book
Activities
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B-KUL-H00H2a Uncertainty Reasoning in Knowledge Systems: Exercises | |||
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B-KUL-H02D2a Uncertainty Reasoning in Knowledge Systems: Lecture | |||
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B-KUL-H08M4a Uncertainty Reasoning in Knowledge Systems: Project | |||
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Evaluation
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B-KUL-H22D2a Evaluation : Uncertainty Reasoning in Knowledge Systems | |||
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