Probabilistic reasoning (2026/2027: Periode 1)

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Cursusdoel

Upon completing this course, the student

  1. recognizes and understands the strengths and weaknesses of probabilistic graphical models (PGMs) 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 and through probabilistic programs;
  4. has knowledge and understanding of methods for constructing probabilistic models for actual applications;
  5. understands and is able to apply techniques for evaluating the robustness and quality of probabilistic models.

Assessment
The course is assessed through

  • a set of practical homework assignments (20% of the final mark)
  • a written test (80%).

Unless you have passed the course, you qualify for a second attempt at the written test if the final grade is at least 4 and the assignment grade at least 6. The practical assignments cannot be re-taken

Prerequisites
This course assumes a background in mathematics and computing at the level of a BSc in Computer Science.

To be able to fulfill the learning goals of this course, it is necessary to develop a solid understanding of the mathematics underlying PGMs. This means that prior to taking the course you should have sufficient skills to understand, apply and manipulate mathematical formulas, and have basic knowledge of the following:

  • algorithms, graph theory and probability theory (at the level of INFODS Datastructuren), and
  • propositional logic, set theory and mathematical proofs (at the level of INFOB1LI Logica voor informatica), and
  • programming; familiarity with R or Python can be an advantage, but is not required.

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.

Before selecting this course, please check out the content and material used at https://ics-websites.science.uu.nl/docs/vakken/prob/

Vakinhoudelijk

Probabilistic models can be used for reasoning and decision support under uncertainty:
Which exercises are most suitable to improve Alex’s calculus skills?
How long after infection will we detect classical swine fever on this farm?
What is the risk of Mr. Johnson developing a coronary heart disease?
Should Mrs. Peterson be given the loan she requested?
Will a "study advisor 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 Computer Science and 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 focus on probabilistic graphical models such as Bayesian networks, their underlying theory, and discuss issues and methods related to the construction of such networks for real-life applications.
In addition, we consider methods for probabilistic inference, including the role of Probabilistic Programming.

Course form
Lectures, self-assessment exercises.

Literature

  • syllabus 'Probabilistic Reasoning with Bayesian networks'
  • course slides
  • selected articles

All are mandatory and will be made available through Brightspace.

Werkvormen

Hoorcollege

Toetsing

Eindresultaat

Verplicht | Weging 100% | ECTS 7,5

Ingangseisen en voorkennis

Ingangseisen

Je moet een geldige toelatingsbeschikking hebben

Voorkennis

Er is geen informatie over benodigde voorkennis bekend.

Voertalen

  • Engels

Cursusmomenten

Gerelateerde studies

Tentamens

Er is geen tentamenrooster beschikbaar voor deze cursus

Verplicht materiaal

Er is geen informatie over de verplichte literatuur bekend


Aanbevolen materiaal

Er is geen informatie over de aanbevolen literatuur bekend

Coördinator

dr. S. Renooij S.Renooij@uu.nl

Docenten

dr. M.I.L. Vákár m.i.l.vakar@uu.nl
dr. S. Renooij S.Renooij@uu.nl

Inschrijving

Deze cursus is open voor bijvakkers. Controleer wel of er aanvullende ingangseisen gelden.

Inschrijving
Van maandag 8 juni 2026 tot en met vrijdag 26 juni 2026

Na-inschrijving
Van maandag 17 augustus 2026 tot en met maandag 21 september 2026

Inschrijving niet geopend
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