General information

• Study points: 0.50
• Language: English
• Category: Exercises
• Duration: 15.0 hours 
• Periodicity: Taught in the first semester
• POC: POC Artificial Intelligence

Taught by

Bruyninckx Herman
De Laet Tinne
De Raedt Luc

Content

There are 6 exercises sessions (mostly with pen and pencil) on various aspects of uncertainty reasoning and graphical models.

General information

• Study points: 3.00
• Language: English
• Category: Lectures
• Duration: 16.5 hours 
• Periodicity: Taught in the first semester
• POC: POC Artificial Intelligence

Taught by

Bruyninckx Herman
De Laet Tinne
De Raedt Luc

Content

Bayesian probability theory: modeling, 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)

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

Applications

General information

• Study points: 0.50
• Language: English
• Category: Assignments
• Duration: 15.0 hours 
• Periodicity: Taught in the first semester
• POC: POC Artificial Intelligence

Taught by

Bruyninckx Herman
De Laet Tinne
De Raedt Luc

Course activities

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

Evaluation description

Examination type:
    written
When?: interim evaluations plus final examination during examination period
Evaluation type:
    Open book
    Presentation

Explanation

The evaluation consists of 
    an open book exam (during the exam period)
    an oral presentation of the results of the project