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Probabilistic reasoning

Website:website containing additional information
Course code:INFOPROB
Credits:7.5 ECTS
Period:period 1 (week 36 through 45, i.e., 4-9-2017 through 10-11-2017; retake week 1)
Timeslot:D15
Participants:up till now 21 subscriptions
Schedule:Official schedule representation can be found in Osiris
Teachers:
formgrouptimeweekroomteacher
lecture   Wed 13.15-15.0037 DDW-1.30 Silja Renooij
 
38 BBG-083
39-44 DDW-1.30
Fri 11.00-12.4536 BBG-001
37-40 BBG-209
41 ANDRO-C101
42-43 BBG-209
44 BBG-214
Exam:
week: 45Fri 10-11-201713.30-16.30 uurroom: EDUC-MEGARON
week: 1Fri 5-1-201813.30-16.30 uurroom: BBG-079retake exam
Contents:

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 studyadvisor-support tool advise you to take this course?

Human experts have to make judgments and decisions based on uncertain, and often even conflicting, information. To support these complex decisions, knowledge-based systems 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 Bayesian networks, their definition and reasoning, and discuss issues and methods related to the construction of such networks for real-life applications.

Warning: This course requires abstract thinking and mathematical skills to understand the many formulas and their manipulation. More specifically, it builds on a basic understanding of the concept of probability and associated rules. You can have a look at the course slides to get an impression of the level of mathematics involved.

Literature:1. Syllabus 'Probabilistic Reasoning': (older) paper edition possibly still sold by A-Eskwadraat; up-to-date online edition available through the website with additional information
2. Studymanual, available online;
3. Course slides, also available online.
Course form:- Lectures (twice a week).
- Self-assessment exercises.
Exam form:Four practical assignments (15% in total) and one written exam (85%). If you are registered for the course, you are automatically entitled to partaking in these exams. Those who are allowed to do a substitute exam (see re-examination conditions) are automatically registered for this.

Note that course registration proceeds through OSIRIS and generally closes about 8 weeks before the start of the course (see registration dates)! If you are planning on taking this course in your first year, you will be notified how to register during the Master Introduction. The lecturer has no OSIRIS-access and cannot do the registration for you.
Minimum effort to qualify for 2nd chance exam:The conditions under which a second chance exam is offered are available on the website with additional information.
Description:In this course, we will consider the theory and applicability of Bayesian 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 Bayesian 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 Bayesian 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 Bayesian 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.
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