N_MaTR Mathematical Decision Theory

University of Finance and Administration
Summer 2017
Extent and Intensity
2/0. 6 credit(s). Type of Completion: zk (examination).
Teacher(s)
prof. Ing. Petr Berka, CSc. (seminar tutor)
Guaranteed by
prof. Ing. Petr Berka, CSc.
Department of Computer Science and Mathematics – Departments – University of Finance and Administration
Contact Person: Ing. Barbora Ptáčková
Timetable of Seminar Groups
N_MaTR/vAPH: Fri 10. 2. 15:45–17:15 S14, 17:30–19:00 S14, Fri 24. 2. 17:30–19:00 S14, Sat 11. 3. 9:45–11:15 S14, 11:30–13:00 S14, P. Berka
Prerequisites
There are no prerequisites for this course.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The goal of the course is to introduce principles of decision-making in intelligent systems.
At the end of the course students should be able to:
- explain basic approaches to problem solving using state space
- describe different decision strategies
- explain knowledge representation and reasoning methods
- describe methods for expressing uncertainty
- explain basic principles of machine learning and adaptation
- describe approaches to building agent systems
Syllabus
  • - Blind state space search
  • - Heuristic state space search
  • - Game theory
  • - Knowledge representation
  • - Knowledge processing
  • - Ucertainty reprezentation
  • - Decision strategies
  • - Uncertainty processing
  • - Machine learning
  • - Learning and adaptation
  • - Reactive agents
  • - Deliberative agents
Literature
    required literature
  • Berka, Petr: Inteligentní Systémy, VŠE Praha, 2008. ISBN 978-80-245-1436-9
    not specified
  • Russell S., Norvig P.: Artificial Intelligence. A Modern Approach. 3. edition, Pearson, 2009
Teaching methods
- lectures and seminars in full-time study,
- tutorials in part-time study,
- case study on application of intelligent systems
Lectures and seminars in full-time study; tutorials in part-time study; compulsory seminar participation is 75% in full-time study, compulsory tutorial participation is 50% in part-time study
Assessment methods
- written final test consisting of unstructured questions (min 60% points)
- case study on application of intelligent systems
Language of instruction
Czech
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
Information on the extent and intensity of the course: 10 hodin KS/semestr.
The course is also listed under the following terms Winter 2007, Winter 2008, Summer 2009, Winter 2009, Winter 2010, Winter 2011, Winter 2012, Winter 2013, Winter 2014, Winter 2015, Winter 2016, Summer 2018, Summer 2019, Summer 2020.
  • Enrolment Statistics (Summer 2017, recent)
  • Permalink: https://is.vsfs.cz/course/vsfs/summer2017/N_MaTR