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252-0209-00L 8 Credits BSC , MSC D-MATH , D-INFK

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Algorithms, Probability, and Computing

Lecturers & Examiners: Prof. Dr. Bernd Gärtner, Prof. Dr. David Steurer, Prof. Dr. Rasmus Kyng, Prof. Dr. Vera Traub

VVZ CR 2.91

Last Updated: 2026-06-01 11:31:15

Abstract

Advanced design and analysis methods for algorithms and data structures: Random(ized) Search Trees, Point Location, Minimum Cut, Linear Programming, Randomized Algebraic Algorithms (matchings), Probabilistically Checkable Proofs (introduction).

Objective

Studying and understanding of fundamental advanced concepts in algorithms, data structures and complexity theory.

Resources

Lecture Notes

Will be handed out.

Literature

Introduction to Algorithms by T. H. Cormen, C. E. Leiserson, R. L. Rivest; Randomized Algorithms by R. Motwani und P. Raghavan; Computational Geometry - Algorithms and Applications by M. de Berg, M. van Kreveld, M. Overmars, O. Schwarzkopf.

Learning Materials (Links)

Main link
Information

General Information

Language: English
Levels: BSC , MSC
Frequency: Yearly recurring

Examination

Type: session examination
Mode: written 180 minutes
Aids: Keine Hilfsmittel erlaubt.

There will be an optional written midterm exam and a written final exam. Script or any other supplementary material for either exam is not permitted. Furthermore, we will hand out two special assignments (compulsory continuous performance assessment) whose solution (typeset in LaTeX) is due two weeks later and will be graded.The final grade is 20% midterm exam + 20% special assignments + 60% final examORif the result of the midterm exam does not improve the final grade or has not been sitted:20% special assignments + 80% final exam

Course Components

Type	Title	Time & Place	Hours
lecture	Algorithms, Probability, and Computing	Mon 14:15-16:00 (ML D 28) Tue 14:15-16:00 (ML D 28)	4 h weekly
exercise	Algorithms, Probability, and Computing	Wed 14:15-16:00 (CAB G 56) Wed 14:15-16:00 (CAB G 57) Wed 16:15-18:00 (CAB G 56) Wed 16:15-18:00 (CAB G 57)	2 h weekly
independent project	Algorithms, Probability, and Computing Project Work, no fixed presence required.	No time listed	1 h weekly

Offered In

Rechnergestützte Wissenschaften Bachelor
- Wahlfächer (Von den angebotenen Wahlfächern müssen mindestens zwei Lerneinheiten erfolgreich abgeschlossen werden.)
Informatik Bachelor
- Kernfächer
  - Vertiefung Theoretical Computer Science
Mathematik Bachelor
- Kernfächer
  - Kernfächer aus Bereichen der angewandten Mathematik ... (vollständiger Titel: Kernfächer aus Bereichen der angewandten Mathematik und weiteren anwendungsorientierten Gebieten)
Rechnergestützte Wissenschaften Master
- Wahlfächer (Von den angebotenen Wahlfächern müssen mindestens zwei Lerneinheiten erfolgreich abgeschlossen werden. Als Wahlfächer für Rechnergestützte Wissenschaften Master gelten automatisch (ohne Anrechnungsgesuch) auch alle Kernfächer/Vertiefungsfächer (aber nicht Wahlfächer!) aus folgenden Studiengängen: Informatik Master Mathematik Master Physik Master Elektrotechnik und Informationstechnologie Master Data Science Master Robotics, Systems and Control Master Statistik Master Neural Systems and Computation Master gemäss den angegebenen Abschnittsreferenzen.)