A course is the basic teaching unit, it's design as a medium for a student to acquire comprehensive knowledge and skills indispensable in the given field. A course guarantor is responsible for the factual content of the course.
For each course, there is a department responsible for the course organisation. A person responsible for timetabling for a given department sets a time schedule of teaching and for each class, s/he assigns an instructor and/or an examiner.
Expected time consumption of the course is expressed by a course attribute extent of teaching. For example, extent = 2 +2 indicates two teaching hours of lectures and two teaching hours of seminar (lab) per week.
At the end of each semester, the course instructor has to evaluate the extent to which a student has acquired the expected knowledge and skills. The type of this evaluation is indicated by the attribute completion. So, a course can be completed by just an assessment ('pouze zápočet'), by a graded assessment ('klasifikovaný zápočet'), or by just an examination ('pouze zkouška') or by an assessment and examination ('zápočet a zkouška') .
The difficulty of a given course is evaluated by the amount of ECTS credits.
The course is in session (cf. teaching is going on) during a semester. Each course is offered either in the winter ('zimní') or summer ('letní') semester of an academic year. Exceptionally, a course might be offered in both semesters.
The subject matter of a course is described in various texts.
NIE-DSS Decision Support Systems Extent of teaching: 2P+1C Instructor: Pavlíčková P., Pergl R. Completion: Z,ZK Department: 18102 Credits: 5 Semester: Z Annotation:
The aim of the course is to provide students with knowledge and skills in decision support systems, their classification (Powerova), selected principles of data-oriented, model-oriented and knowledge-oriented decision support systems. Students will also gain knowledge of multicriterial decision-making methods and game theory. They will also learn about the principles of conceptually and ontologically oriented decision support systems and the basics of distribution, optimization and evolution methods and algorithms.
Lecture syllabus:
1. Principles of decision support systems, Power classification, decision process and its phases. 2. Decision theory (under certainty, uncertainty, risk), multi-criteria decision making. 3. Game theory and decision models. 4. Business Intelligence (BI) systems, application areas and application of BI in business management. 5. Principles of dimensional modeling, design of indicators and dimensions. Implementation of BI tasks. 6. Conceptually and ontologically oriented decision support systems. 7. Executive IS, expert systems, systems for group decision support. 8. Operational research support systems: optimization and distribution methods, evolutionary algorithms. 9. Knowledge and knowledge management, knowledge models, knowledge maps, business knowledge systems. 10. Investment decisions, dynamic methods of investment evaluation. 11. Risk management and risk management support systems. 12. Fuzzy sets, artificial intelligence, spatial decision support systems (GIS, forecasting systems). 13. Communication and web oriented decision support systems, groupware systems, virtual organizations. Seminar syllabus:
1. Principles of decision support systems, Power classification, decision process and its phases. 2. Decision theory (under certainty, uncertainty, risk), multi-criteria decision making. 3. Game theory and decision models. 4. Business Intelligence (BI) systems, application areas and application of BI in business management. 5. Principles of dimensional modeling, design of indicators and dimensions. Implementation of BI tasks. 6. Conceptually and ontologically oriented decision support systems. 7. Executive IS, expert systems, systems for group decision support. 8. Operational research support systems: optimization and distribution methods, evolutionary algorithms. 9. Knowledge and knowledge management, knowledge models, knowledge maps, business knowledge systems. 10. Investment decisions, dynamic methods of investment evaluation. 11. Risk management and risk management support systems. 12. Fuzzy sets, artificial intelligence, spatial decision support systems (GIS, forecasting systems). 13. Communication and web oriented decision support systems, groupware systems, virtual organizations. Literature:
1. Burstein, F. : Handbook on Decision Support Systems 1: Basic Themes. Springer, 2008. ISBN 978-3-540-48712-8. 2. Burstein, F. : Handbook on Decision Support Systems 2: Variations. Springer, 2008. ISBN 978-3-540-48715-9. 3. Clyde W. Holsapple, Andrew B. Whinston : Decision support systems: a knowledge-based approach. West Group, 1996. ISBN 978-0314065100. 4. David Schuff, David Paradice : Decision Support: An Examination of the DSS Discipline. Springer, 2010. ISBN 978-1-4419-6180-8. Requirements:
No special entry requirements are required to complete the course.
Information about the subject and teaching materials can be found at https://moodle-vyuka.cvut.cz/link/course.php?cx=NIE-DSS The course is also part of the following Study plans:
Study Plan Study Branch/Specialization Role Recommended semester NIE-SI.21 Software Engineering 2021 PV 3
Page updated 19. 4. 2024, semester: L/2020-1, L/2021-2, Z/2023-4, Z/2024-5, Z/2019-20, Z/2022-3, L/2019-20, L/2022-3, Z/2020-1, Z/2021-2, L/2023-4, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška