Bachelor
Informatica
Informatiekunde
Kunstmatige intelligentie
Master
Computing Science
Game&Media Technology
Artifical Intelligence
Human Computer Interaction
Business Informatics

Website: | website containing additional information | ||||||||||||||||||||

Course code: | INFOMADS | ||||||||||||||||||||

Credits: | 7.5 ECTS | ||||||||||||||||||||

Period: | period 1 (week 36 through 45, i.e., 3-9-2018 through 9-11-2018; retake week 1)
| ||||||||||||||||||||

Timeslot: | C | ||||||||||||||||||||

Participants: | up till now 82 subscriptions | ||||||||||||||||||||

Schedule: | Official schedule representation can be found in Osiris | ||||||||||||||||||||

Teachers: | Dit is een oud rooster!
| ||||||||||||||||||||

Contents: | The purpose of is to teach topics that: - are important for the working area of algorithms (in practice and theory)
- are prerequisites for other courses in the COSC program
- that are not encountered by all students in the bachelor.
In many real-life decision problems in e.g. (public) transportation, logistics, energy networks, healthcare, computer networks and education we want to select a very good solution from a large set of possible solutions. In the course you learn how to model such problems and how to solve them by well-known (simulation) algorithms. We focus on discrete models. You learn about the theoretical complexity and about the possibilities for exact optimization algorithms, heuristics and what-if analysis.
For stochastic problems, we study The learning outcomes of the course are - Knowledge of discrete-event simulation models and combinatorial optimization models
- Knowledge of methods for experimental research with discrete-event simulation including statistical methods
- Insight in the complexity of combinatorial optimization problems
- Knowledge of well-known types of combinatorial optimization algorithms
- Ability to model problems from applications as a discrete-event simulation problem and as a combinatorial optimization problem
- Ability to perform a scientific sound simulation study including statistical analysis
- Ability to apply the algorithms from the course to combinatorial optimization problems
| ||||||||||||||||||||

Literature: | Slides completed by your own lecture notes . The following books are not mandatory but interesting for further reading: - The lectures on simulation are based on Simulation modeling and analysis, A.M. Law, McGraw-Hill Higher Education, 2015, ISBN 978-1-259-25438-3 (fifth edition) (you can also use an older edition).
- Integer Programming, Laurence A. Wolsey, Wiley-Interscience publication, 1998, ISBN 0-471-28366-5.
- Computers and Intractability: A Guide to the Theory of NP-Completeness. M.R. Garey and D.S. Johnson, W.H. Freeman and Company, New York, 1979, ISBN 0-7167-1044-7.
- Algorithm Design. John Kleinberg, Eva Tardos, Pearson/Addision Wesley, 2005. ISBN 0-321-29535-8.
| ||||||||||||||||||||

Course form: | Lectures, self-study, exercises, assignments. | ||||||||||||||||||||

Exam form: | In the grading the simulation assignment contributes 50% and the written exam contributes 50%. To get a grade of at least 6 the following is required:
- Simulation assignment: completeness of the report, this means that it has to contain all the parts given in the workplan
- Final written exam: minimal required grade: unrounded 5.
- Participation in mandatory sessions and meetings (indicated in the week schedule)
| ||||||||||||||||||||

Minimum effort to qualify for 2nd chance exam: | For additional testing on the assignment, explicit permission of the teacher is required. |