| Website: | website containing additional information |
| Course code: | INFOPROB |
| Credits: | 7.5 ECTS (=5.25 old credit points) |
| Period: | periode 1 (week 36 t/m 45, dwz 8-9-2005 t/m 11-11-2005; herkansing week 1)
|  |
| Timeslot: | D |
| Participants: | up till now 32 subscriptions |
| Schedule: | Dit is een oud rooster!
|
| Contents: | Human experts can make judgments and decisions based on uncertain, and often even conflicting,
information. A knowledge-based system that is required to perform at least at a similar level of expertise,
should be able to cope with this type of information. For this reason, formalisms 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; it is therefore no surprise that the applicability of this mathematical theory as a model for
reasoning under uncertainty plays an important role.
In this course, we will consider probabilistic models for manipulating uncertain information in
knowledge-based systems. More specifically, we will consider the theory underlying the framework of
probabilistic networks, and discuss the construction of such networks for real-life applications. |
| Literature: | Syllabus 'Probabilistic Reasoning', papers, and
transparencies (all online; see website with additional information). |
| Course form: | Lectures (twice a week), and an exercise class. |
| Exam form: | One written exam (closed book!). |
| Minimum effort to qualify for 2nd chance exam: | Om aan de aanvullende toets te mogen meedoen is ontbreken van ten hoogte 1 toetsactiviteit toegestaan. |
| Description: | In this course, we will consider the theory and applicability of probabilistic
networks. The course roughly consists of three parts. As an introduction to
probabilistic networks, the first part of the course deals with independence
relations and their graphical represenation by means of undirected and directed
graphs. The second part introduces the probabilistic network as a compact
representation of a probability distribution on a set of statistical variables; in
addition, the Pearl algorithm for computing probabilities from a probabilistic
network is discussed. The algorithm allows for calculating the probability of any
value of an arbitrary variable in the network, with or without incorporating
observations for one or more variables. The third part of this course concerns
the construction of probabilistic networks for real-life applications. Topics
covered include both automated construction of networks from data, and
handcrafting the network with the help of domain experts. |
