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Algorithms for decision support

Website:website containing additional information
Course code:INFOMADS
Credits:7.5 ECTS
Period:period 1 (week 36 through 45, i.e., 3-9-2020 through 6-11-2020; retake week 1)
Timeslot:C
Participants:up till now 108 subscriptions
Schedule:Official schedule representation can be found in MyTimetable
Teachers:
formgrouptimeweekroomteacher
lecture          Hans Bodlaender
Alison Liu
tutorial          Jens Heuseveldt
group 1        Marieke van der Wegen
#AIAC
Contents:

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 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 optimization algorithms. We focus on discrete models. You learn about computational complexity and about meaning of exact optimization algorithms, heuristics and what-if analysis. For deterministic problems, we study well-known algorithms from combinatorial optimization, including algorithms for linear programming, integer linear programming, online algorithms, shortest paths, approximation.

Literature:In this course, you learn a number of important techniques and results from the field of algorithms, including linear programming, integer linear programming, approximation, NP-completeness.
Course form:Lectures, self-study, project
Exam form:There is a project done in groups (50 percent) and an exam (50 percent). Details of the grading rules can be found on the Blackboard website.
Minimum effort to qualify for 2nd chance exam:See the Blackboard website to see for the rules to qualify for the 2nd chance exam.
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