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WiSe 2019/20

Search-Based Software Engineering - Einzelansicht

  • Funktionen:
Grunddaten
Veranstaltungsart Vorlesung SWS 3
Veranstaltungsnummer 417290001 Max. Teilnehmer/-innen
Semester SoSe 2019 Zugeordnetes Modul
Erwartete Teilnehmer/-innen
Rhythmus jedes 2. Semester
Hyperlink  
Weitere Links https://www.uni-weimar.de/de/medien/professuren/intelligente-softwaresysteme/
Sprache englisch
Termine Gruppe: [unbenannt]
  Tag Zeit Rhythmus Dauer Raum Raum-
plan
Lehrperson Bemerkung fällt aus am Max. Teilnehmer/-innen
Einzeltermine anzeigen
Mo. 09:15 bis 10:45 wöch. von 08.04.2019  Bauhausstraße 11 - Seminarraum 015  

Lecture

 
Einzeltermine anzeigen
Mo. 09:00 bis 11:00 Einzel am 22.07.2019 Marienstraße 13 C - Hörsaal D  

exam

 
Einzeltermine anzeigen
Di. 11:00 bis 12:30 wöch. von 02.04.2019  Bauhausstraße 11 - Seminarraum 014  

Lab class

 
Gruppe [unbenannt]:
 
 


Zugeordnete Person
Zugeordnete Person Zuständigkeit
Siegmund, Norbert, Prof., Dr.-Ing.
Studiengänge
Abschluss Studiengang Semester Leistungspunkte
Master MediaArchitecture (M.Sc.), PV18 - 6
Master Digital Engineering (M.Sc.), PV 19 - 6
Master Computer Science for Digital Media (M.Sc.), PV 18 - 4,5
Master Medieninformatik (M.Sc.), PV 29 - 4,5
Master Computer Science and Media (M.Sc.), PV 11 - 4,5
Master Computer Science for Digital Media (M.Sc.), PV 17 - 4,5
Master Digital Engineering (M.Sc.), PV 17 - 6
Master Human-Computer Interaction (M.Sc.), PV14 - 4,5
Master Human-Computer Interaction (M.Sc.), PV15 - 4,5
Master Human-Computer Interaction (M.Sc.), PV17 - 4,5
Master MediaArchitecture (M.Sc.), PV14 2 - 2 3
Zuordnung zu Einrichtungen
Fakultät Medien
Intelligente Softwaresysteme
Inhalt
engl. Beschreibung/ Kurzkommentar

Search-Based Software Engineering

Search-Based Software Engineering is about learning and optimizing complex tasks that are computationally intractable for exact methods. The goal of this course is to understand the principles of meta-heuristics in optimization as well as on handling constraints and dimensionality.

 

Students should understand the following techniques and theories:

  • Problem space exploration and search-based optimization
  • Meta-heuristics for single and multiple objective optimization
  • Relationship between biological learning and optimization with algorithms
  • Dimensionality-reduction techniques
  • Constraint resolution

 

Students should be able to apply the above theories for solving concrete learning and optimization problems. Furthermore, they should appreciate the limits and constraints of the individual methods above.

 

Students should be able formalize and generalize their own solutions using the above concepts and implement them in a specified language (preferable in Python).

 

Students should master concepts and approaches such as

  • Simulated annealing
  • Swarm optimization
  • Ant colonization
  • Evolutionary algorithms
  • Dimensionality Reduction (PCA + Feature Subset Selection)
  • Constraint Satisfaction Problem Solving

 

in order to tackle problems learning and optimizing huge problems, which are inherent to Digital Media. They should also be able to implement the algorithms and techniques in Python and be able to understand a proposed problem, to compare different approaches and techniques regarding applicability and accuracy, to make well-informed decisions about the preferred solution and, if necessary, to find their own solutions.

 

Students should develop an understanding of the current state of research in optimization and learning. With appropriate supervision, students should be able to tackle new research problems, especially in the area of search-based software engineering.

Literatur

Handbook of Metaheuristics, Fred W. Glover, Gary A. Kochenberger, Springer Science & Business Media, 2006.

Machine Learning, Tom Mitchell, McGraw-Hill Education, 1997.

Essentials of Metaheuristics, Sean Luke, 2013.

A Field Guide to Genetic Programming, Riccardo Poli, William B. Langdon, Nicholas Freitag McPhee, Lulu Pr, 2008.

Make Your Own Neural Network, Rashid, Tariq, CreateSpace Independent Publishing Platform, 2016.

http://neuralnetworksanddeeplearning.com/index.html (online book)

Bemerkung

Ehemals "Machine Learning for Software Engineering". Dieser Kurs kann daher nur belegt werden, wenn der Kurs "Machine Learning for Software Engineering (417130002)" noch nicht erfolgreich abgeschlossen wurde.

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Formely known as "Machine Learning for Software Engineering". Therefore the class can only be taken, if the class "Machine Learning for Software Engineering (417130002)" has not yet been sucessfully completed.

Voraussetzungen

BSc in a relevant study field

Leistungsnachweis

Written or oral examination. Participation requires the successful completion of the course labs (tasks over the semester). Digital Engineering students will be required to successfully complete an additional project.

Zielgruppe

M.Sc. Medieninformatik / Computer Science and Media / Computer Science for Digital Media

M.Sc. Digital Engineering

M.Sc. Human-Computer Interaction


Strukturbaum
Keine Einordnung ins Vorlesungsverzeichnis vorhanden. Veranstaltung ist aus dem Semester SoSe 2019 , Aktuelles Semester: WiSe 2019/20

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