Lehrveranstaltungen WiSe 2023/2024

Industriemathematik, B.Sc.

Veranstaltungen anzeigen: alle | in englischer Sprache | für ältere Erwachsene | mit Nachhaltigkeitszielen

Bachelor 4. Semester und höher

Modul: Fortgeschrittene Themen Industriemathematik (9 CP)

Pflichtmodul, welches im 5. Semester belegt werden sollte. Dazu muss EINE der zugehörigen Veranstaltungen belegt werden, wobei dieses Semester aus folgenden Veranstaltungen gewählt werden kann:
VAKTitel der VeranstaltungDozentIn
03-M-SP-2Basics of Mathematical Statistics (Statistics I) (in englischer Sprache)


wöchentlich Mo 10:00 - 12:00 MZH 7200 MZH 1450 Lecture
wöchentlich Mo 14:00 - 16:00 MZH 1380/1400 Exercise
wöchentlich Do 10:00 - 12:00 MZH 7200 Lecture

Do 14.03.24 09:30 - 12:30 MZH 6200
Prof. Dr. Thorsten-Ingo Dickhaus

General Studies - Fachergänzende Studien

Fachergänzendes Studienangebot aus der Mathematik bzw. Industriemathematik.
VAKTitel der VeranstaltungDozentIn
03-IBFW-HTO (03-BE-699.12)Hands-on Tutorial on Optimization (in englischer Sprache)


Mo 09.10.23 - Fr 13.10.23 (Mo, Di, Mi, Do, Fr) 09:00 - 17:00 MZH 5500
A large number of problems arising in practical scenarios like communication, transportation, planning, logistics etc. can be formulated as discrete linear optimization problems. This course briefly introduces the theory of such problems. We develop a toolkit to model real-world problems as (discrete) linear programs. We also explore several ways to find integer solutions such as cutting planes, branch & bound, and column generation.

Throughout the course, we learn these skills by modeling and solving, for example, scheduling, packing, matching, routing, and network-design problems. We focus on translating practical examples into mixed-integer linear programs. We learn how to use solvers (such as CPLEX and Gurobi) and tailor the solution process to certain properties of the problem.

This course consists of two phases:

  • One week Mon-Fri (full day) of lectures and practical labs: October 9-13, 2023, in MZH.
  • A subsequent project period: One problem has to be modeled, implemented, and solved individually or in a group of at most three students. The topic will be provided by the lecturers and will be discussed on the last day of the block course. The project including the implementation has to be presented in the beginning of the winter semester.

There are no prerequisites except some basic programming skills to participate.

Please confirm your participation by email to Felix by September 15.

Prof. Dr. Nicole Megow
Dr. Felix Christian Hommelsheim
03-M-GS-14Starting Data Science in R (in englischer Sprache)
a course on R programming and data science methods with practicals and projects


wöchentlich Mi 14:00 - 16:00 MZH 2490 (Seminarraum) Seminar

Mi 06.03.24 14:00 - 16:00 ZOOM-Projektpräsentationen I
Mi 20.03.24 14:00 - 16:00 ZOOM Projektpräsentationen II

The course provides an introductory level of programming skills in R.
Students are welcome to present own ideas, data and projects. I expect a project report or a method talk with demo on own data. Practicals in "R" will work also on synthetic data to illustrate methods features, limitations and differences.

Prof. Dr. Stephan Frickenhaus