Learning goals (2020-2021)

Upon completing this course, the student
1. recognizes and understands the strengths and weaknesses of probabilistic graphical models in general and Bayesian networks in particular;
2. understands the relation between probabilistic independence and the graphical representations thereof, and is able to draw conclusions from this relation;
3. understands and is able to apply probabilistic inference in Bayesian networks;
4. has knowledge and understanding of methods for constructing the Bayesian network graph for actual applications;
5. has knowledge and understanding of various methods for quantifying Bayesian networks, including parameterized models (noisy-or), together with their benefits and limitations;
6. understands and is able to apply techniques for evaluating the robustness and quality of a Bayesian network.

Please note: To be able to fulfill the learning goals of this course, a solid understanding of the mathematics underlying PGMs is required. This means you should have sufficient mathematical skills to understand, apply and manipulate the many formulas that are presented. (You can have a look at the course slides to get an impression of the level of mathematics involved.) In addition, it is assumed that you are capable of abstracting away from given examples, applying the knowledge and techniques learned to contexts other than those discussed in class.

Grading
A set of practical homework assignments (15% in total) and one written test (85%). If you are registered for the course, you are automatically entitled to partaking in these assessments. Those who are allowed to do a substitute test (see re-examination conditions) are automatically registered for this.
 

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Will a studyadvisor-support tool advise you to take this course?

In complex domains, people have to make judgments and decisions based on uncertain, and often even conflicting, information; a difficult task, even for experts in the domain. To support these complex decisions, knowledge-based systems should be able to cope with this type of information. For this reason, models for representing uncertainty and algorithms for manipulating uncertain information are important research subjects within the field of Artificial Intelligence. Probability theory is one of the oldest theories dealing with the concept of uncertainty and therefore plays an important role in many decision support systems.

In this course, we will consider probabilistic models for representing and reasoning under uncertainty. More specifically, we will consider the theory underlying the framework of Bayesian networks, their definition and reasoning, and discuss issues and methods related to the construction of such networks for real-life applications.

The course roughly consists of three parts. As a general introduction to (probabilistic) graphical models, the first part of the course deals with independence relations and their representation by means of undirected and directed graphs. The second part introduces the Bayesian network as a compact representation of a probability distribution on a set of statistical variables; in addition, algorithms that allow for efficiently computing probabilities from a Bayesian network are discussed. The third part of the course concerns the construction of Bayesian networks for real-life applications. Topics covered include automated construction of networks from data, handcrafting the network with the help of domain experts, robustness analysis and evaluation.

Course form
Lectures (twice a week), self-assessment exercises

Literature -mandatory:
1. Syllabus 'Probabilistic Reasoning with Bayesian networks': paper edition sold by A-Eskwadraat, up-to-date online edition available through the course website
2. Course slides, available online.

Literature - additional
Studymanual, also available online.