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 graphical model approach for dealing with uncertainty; they are 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 modelling, inference and learning. They are familiar with applications of these techniques.
The student understands how techniques for reasoning about uncertainty can be integrated with logic for reasoning and learning.
Lecture
Bayesian probability theory: modelling, inference, reasoning, decision making Graphical models – Bayesian networks, Markov Networks and Factors Graphs Independence in graphical models Inference algorithms Hidden and observable parameters Learning Dynamic systems (such as Hidden Markov Models and Kalman Filters) Combining logic with graphical models Applications
Project
Each year students have to make one or more assignments and hand in their solution. This can take the form of traditional exercises or of a small project with software for graphical models.
Evaluation
The evaluation consists of
closed book exam (with the use of a formularium, during the exam period, by far the most important part of the evalution), and reports on the assignments.