Website:  website containing additional information 
Course code:  INFOMADS 
Credits:  7.5 ECTS 
Period:  period 1 (week 36 through 45, i.e., 292019 through 8112019; retake week 1 (bachelor) / 2 (master))
 
Timeslot:  C 
Participants:  up till now 119 subscriptions 
Schedule:  Official schedule representation can be found in MyTimetable 
Teachers:  Dit is een oud rooster!

Contents:  Due to illness, there is no lecture on Thursday September 5!!!
In data science we distinguish:
 Descriptive analysis: what is the current situation?
 Predictive analysis: what is going to happen in the future?
 Prescriptive analysis: which decision should I make?
This course and other courses in the field of algorithms focus on prescriptive analysis .
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.
It therefore contains a broad range of topics.
In many reallife 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 simulation and/or optimization algorithms. We focus on discrete models. You learn about computational complexity and about meaning of exact optimization algorithms, heuristics and whatif analysis.
For deterministic problems, we study wellknown algorithms from combinatorial optimization. In particular, we study integer linear programming, since it is one of the most frequently applied techniques in practice. For stochastic problems, we study discreteevent simulation . As assignment you have to perform a simulation study of the Uithoflijn, the new tram line that will connect Utrecht CS and the Uithof.
The learning outcomes of the course are
 Knowledge of discreteevent simulation models and combinatorial optimization models
 Knowledge of methods for experimental research with discreteevent simulation including statistical methods
 Insight in the complexity of combinatorial optimization problems
 Knowledge of wellknown types of combinatorial optimization algorithms
 Ability to model problems from applications as a discreteevent 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:  The material of the course consists of:
 Simulation : slides, references to sections of the book by Law (copies of the most important section are available), exercises, and some solutions. There is also background material on statistics.
 Integer linear programming : slides, lecture notes, exercises, and solutions.
 Complexity: slides, exercises, and solutions.
Since there will be a lot of examples on the blackboard (especially on integer linear programming) it is strongly recommended that you make your own lecture notes .
For your learning experience, solutions to exercises will always be published some time later than the exercises. There will be solutions for a major part of (but not all) exercises.
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, McGrawHill Higher Education, 2015, ISBN 9781259254383 (fifth edition)
(you can also use an older edition).
 Integer Programming, Laurence A. Wolsey, WileyInterscience publication, 1998, ISBN 0471283665.
 Computers and Intractability: A Guide to the Theory of NPCompleteness. M.R. Garey and D.S. Johnson, W.H. Freeman and Company, New York, 1979, ISBN 0716710447.
 Algorithm Design. John Kleinberg, Eva Tardos, Pearson/Addision Wesley, 2005. ISBN 0321295358.

Course form:  Lectures, selfstudy, 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
 You attended the final feedback discussion in person. It is not sufficient if other members of your group attended.
 Final written exam: minimal required grade: unrounded 5.

Minimum effort to qualify for 2nd chance exam:  Additional examination :
 Minimum required effort:
 Your final grade is at least 4
 You received a pass for the milestones of the simulation assignment
 You attended every milestone meetingĀ in person. It is not sufficient if other members of your group attended.
 Retake for at most one part (out of simulation assignment and exam):
 If exam < 5, then retake exam
 If exam < 6 and retake exam >= 6 would be sufficient, then retake exam
 Otherwise, either retake exam or repair assignment
 For repair assignment, permission of teacher required
 Maximum grade for repair assignment is 7
 If there are unforeseen extreme circumstances because of which you cannot make a milestone or a meeting, you have to notify the teacher beforehand by email.
