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.
Default requirements for all Master of Artificial Intelligence students.
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.
Is also included in other courses
- Master in de toegepaste informatica (Artificial Intelligence and Databases) 60 ects.
- Master in de ingenieurswetenschappen: biomedische technologie 120 ects.
- Master of Artificial Intelligence (Option: Engineering and Computer Science (ECS)) 60 ects.
- Master in de informatica (uitdovend, enkel 2e fase) (Specialisation: Artificial Intelligence) 120 ects.
- Master in de ingenieurswetenschappen: computerwetenschappen (Specialisation: Artificial Intelligence) 120 ects.
- Master of Engineering: Biomedical Engineering 120 ects.
Bayesian probability theory: modeling, inference, reasoning, decision making
Graphical models -- Bayesian networks, Markov Networks and Factors Graphs
Independence in graphical models
Hidden and observable parameters
Dynamic systems (such as Hidden Markov Models and Kalman Filters)
Introduction to statistical relational learning, combining logic with graphical models
How logic and graphical models can be combined
Knowledge Based Model Construction
Probabilistic Prolog Systems
The evaluation consists of
an open book exam (during the exam period)
an oral presentation of the results of the project