MSE Master of Science in Engineering

The Swiss engineering master's degree

Jedes Modul umfasst 3 ECTS. Sie wählen insgesamt 10 Module/30 ECTS in den folgenden Modulkategorien:

  • ​​​​12-15 ECTS in Technisch-wissenschaftlichen Modulen (TSM)
    TSM-Module vermitteln Ihnen profilspezifische Fachkompetenz und ergänzen die dezentralen Vertiefungsmodule.
  • 9-12 ECTS in Erweiterten theoretischen Grundlagen (FTP)
    FTP-Module behandeln theoretische Grundlagen wie die höhere Mathematik, Physik, Informationstheorie, Chemie usw. Sie erweitern Ihre abstrakte, wissenschaftliche Tiefe und tragen dazu bei, den für die Innovation wichtigen Bogen zwischen Abstraktion und Anwendung spannen zu können.
  • 6-9 ECTS in Kontextmodulen (CM)
    CM-Module vermitteln Ihnen Zusatzkompetenzen aus Bereichen wie Technologiemanagement, Betriebswirtschaft, Kommunikation, Projektmanagement, Patentrecht, Vertragsrecht usw.

In der Modulbeschreibung (siehe: Herunterladen der vollständigen Modulbeschreibung) finden Sie die kompletten Sprachangaben je Modul, unterteilt in die folgenden Kategorien:

  • Unterricht
  • Dokumentation
  • Prüfung
Advanced Algorithms and Data Structures (FTP_AdvAlgDS)

Algorithms are at the heart of every computer program. Informally, an algorithm is a procedure to solve a (computational) problem within a finite number of elementary steps. The same problem can be addressed with different algorithms, hence it is important to compare the different options in order to choose the best one. Experimental analysis is one way to perform such comparison, but it has several limits. The main goal of this class is to learn how to analyze the performance of a given algorithm in a formal mathematical way. We will focus on some fundamental polynomial-time-solvable problems. Along the way, we will study some of the main techniques to design efficient algorithms, among which the use of efficient data structures.

Eintrittskompetenzen

Familiarity with basic discrete math, logic and probability theory. Familiarity with one programming language such as C++, Java, or similar languages.

Lernziele

The main goal of this course is to learn basic techniques to design algorithms and data structures for fundamental polynomial-time-solvable problems, and mathematical tools to analyze their performance from a theoretical point of view.

Modulinhalt

A. Analytical tools: worst-case analysis and asymptotic notation; analysis of recursive algorithms; analysis of randomized algorithms; amortized analysis.

B. Algorithmic techniques: greedy; dynamic programming; divide et impera; randomization; fast data structures.

C. Data structures: search trees; priority queues; union-find; hash tables.

D. Polynomial-time problems: sorting and selection; shortest paths; minimum spanning tree; maximum flow; maximum matching.

Lehr- und Lernmethoden

Interactive lectures both for theory and exercises.

Bibliografie

- T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein. Introduction to Algorithms. MIT Press. Third edition.

- J. Kleinberg, E. Tardos. Algorithm Design. Addison-Wesley.

Vollständige Modulbeschreibung herunterladen

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