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Uncertainty in Artificial Intelligence (B-KUL-H02D2A)

4 ECTSEnglish47 First term
De Raedt Luc (coordinator) |  De Laet Tinne |  De Raedt Luc
POC Artificial Intelligence

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.

Previous knowledge

The student is familiar with the basic concepts of discrete probability and mathematics.
Knowledge of calculus is useful but not required.

Is included in these courses of study

Activities

3 ects. Uncertainty in Artificial Intelligence: Lecture (B-KUL-H02D2a)

3 ECTSEnglishFormat: Lecture17 First term
De Laet Tinne |  De Raedt Luc
POC Artificial Intelligence

Content

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

Course material

The  course is 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 and some additional materials.

0.5 ects. Uncertainty in Artificial Intelligence: Exercises (B-KUL-H00H2a)

0.5 ECTSEnglishFormat: Practical15 First term
De Laet Tinne |  De Raedt Luc
POC Artificial Intelligence

Content

There are around 6 exercise sessions (mostly with pen and paper) on various aspects of uncertainty reasoning and graphical models.

0.5 ects. Uncertainty in Artificial Intelligence: Project (B-KUL-H08M4a)

0.5 ECTSEnglishFormat: Assignment15 First term
De Laet Tinne |  De Raedt Luc
POC Artificial Intelligence

Content

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.

Format: more information

The project consists of one or more assignments, possibly involving tasks with implementations of graphical models.

Evaluation

Evaluation: Uncertainty in Artificial Intelligence (B-KUL-H22D2a)

Type : Partial or continuous assessment with (final) exam during the examination period
Description of evaluation : Written, Report
Type of questions : Open questions
Learning material : List of formulas, Calculator

Explanation


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.

Information about retaking exams

The exam can be retaken but the assignments cannot be retaken.