FI:IV109 Modeling and Simulation - Course Information
IV109 Modeling and SimulationFaculty of Informatics
- Extent and Intensity
- 2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
- doc. Mgr. Radek Pelánek, Ph.D. (lecturer)
RNDr. Jiří Glozar (assistant)
- Guaranteed by
- doc. Mgr. Radek Pelánek, Ph.D.
Department of Machine Learning and Data Processing - Faculty of Informatics
Supplier department: Department of Machine Learning and Data Processing - Faculty of Informatics
- Mon 14:00–15:50 A217
- Timetable of Seminar Groups:
IV109/02: Mon 17. 2. to Fri 15. 5. each odd Thursday 14:00–15:50 B204, R. Pelánek
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 70 student(s).
Current registration and enrolment status: enrolled: 53/70, only registered: 0/70, only registered with preference (fields directly associated with the programme): 0/70
- fields of study / plans the course is directly associated with
- there are 75 fields of study the course is directly associated with, display
- Course objectives
- The course offers a wide overview of computational modeling and gives students a practical experience with computational modeling.
- Learning outcomes
- At the end of the course students will be able to: describe main concepts of complex systems (particularly "feedback loops"); explain main principles and applications of computational modeling; compare modeling approaches; describe well-know case studies in computational modeling; create a computational model.
- Introduction, history, role of modeling and simulation in research, applications. Computational models.
- Complex systems, system thinking, feedback loops.
- System dynamics approach, examples (demographics, Limits to growth).
- Agent based modeling: basic principles, cellular automata, decentralized systems.
- Game theory, models of cooperation. Models of adaptation (genetic algorithms, neural networks).
- Modeling of networks: examples of networks and their properties, models of networks.
- Analysis and evaluation of models.
- Application of modeling from different areas (e.g. economics, traffic, epidemiology, biology).
- recommended literature
- PELÁNEK, Radek. Modelování a simulace komplexních systémů. Jak lépe porozumět světu (Modeling and simulation of complex systems). 1. vyd. Brno: Masarykova univerzita, 2011. 236 pp. mimo edice. ISBN 978-80-210-5318-2. info
- RESNICK, Mitchel. Turtles, termites, and traffic jams : explorations in massively parallel microworlds. Cambridge: Bradford Book, 2000. xviii, 163. ISBN 0-262-68093-9. info
- BARABÁSI, Albert-László. Linked :how everything is connected to everything else and what it means for business, science, and everyday life. New York: Plume Book, 2003. 294 s. ISBN 0-452-28439-2. info
- Teaching methods
- lectures, software labs
- Assessment methods
- 40% project (modeling and simulation of a choosen problem), 40% written exam, 20% reading assignments during semester
- Language of instruction
- Further Comments
- Study Materials
The course is taught annually.
- Teacher's information
- Enrolment Statistics (recent)
- Permalink: https://is.muni.cz/course/fi/spring2020/IV109