|Website:||website containing additional information|
|Period:||period 1 (week 36 through 45, i.e., 3-9-2020 through 6-11-2020; retake week 1)|
|Participants:||up till now 108 subscriptions|
|Schedule:||Official schedule representation can be found in MyTimetable|
In data science we distinguish:
This course and other courses in the field of algorithms focus on prescriptive analysis .
The purpose of is to teach topics that:
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.|