621.061 (20S) Einführung in die Artificial Intelligence I

Sommersemester 2020

Anmeldefrist abgelaufen.

Erster Termin der LV
05.03.2020 14:30 - 16:30 , N.1.43
Nächster Termin:
28.05.2020 14:30 - 16:30 , N.1.43

Überblick

Lehrende/r
LV-Titel englisch
Introduction to Artificial Intelligence I
LV-Art
Vorlesung-Kurs (prüfungsimmanente LV )
Semesterstunde/n
2.0
ECTS-Anrechungspunkte
3.0
Anmeldungen
36 (30 max.)
Organisationseinheit
Unterrichtssprache
Englisch
LV-Beginn
05.03.2020
eLearning
zum Moodle-Kurs

LV-Beschreibung

Intendierte Lernergebnisse

Provides an introduction to general problem solving methods used in artificial intelligence and knowledge-based systems. The course presents a variety of search approaches as well as modern knowledge representation and reasoning systems implementing them.

Lehrmethodik

Classroom instructions mixed with practical exercises. The teaching language is English or German depending on the preferences of the audience. The slides are in English.

Inhalt/e

Covered topics include:

  • Uninformed and informed search methods
  • Overview of incomplete (local) approaches to solving hard problems
  • Knowledge representation and reasoning with constraints programming
  • MiniZinc programming language   
  • Game playing          

Erwartete Vorkenntnisse

Algorithms and data structures

Literatur

  • Stuart Russell and Peter Norvig: Artificial Intelligence: A modern approach. Prentice Hall, 2009
  • Rina Dechter: Constraint Processing. Morgan Kaufmann Publishers, 2003
  • Stefan Edelkamp and Stefan Schrödl: Heuristic search: theory and applications. Elsevier, 2011

Intendierte Lernergebnisse

Provides an introduction to general problem solving methods used in artificial intelligence and knowledge-based systems. The course presents a variety of search approaches as well as modern knowledge representation and reasoning systems implementing them.

Lehrmethodik

Classroom instructions mixed with practical exercises. The teaching language is English or German depending on the preferences of the audience. The slides are in English.

Inhalt/e

Covered topics include:

  • Uninformed and informed search methods
  • Overview of incomplete (local) approaches to solving hard problems
  • Knowledge representation and reasoning with Constraints Programming
  • MiniZinc programming language   
  • Game playing            

Erwartete Vorkenntnisse

Algorithms and data structures

Literatur

  • Stefan Edelkamp and Stefan Schrödl: Heuristic search: theory and applications. Elsevier, 2011
  • Rina Dechter: Constraint Processing. Morgan Kaufmann Publishers, 2003
  • Stuart Russell and Peter Norvig: Artificial Intelligence: A modern approach. Prentice Hall, 2009

Prüfungsinformationen

Beurteilungsschema

Note/Grade Benotungsschema

Position im Curriculum

  • Bachelorstudium Angewandte Informatik (SKZ: 511, Version: 19W.1)
    • Fach: Vertiefung Informatik (Wahlfach)
      • 7.3 Einführung in die Artificial Intelligence I ( 2.0h VC / 3.0 ECTS)
        • 621.061 Einführung in die Artificial Intelligence I (2.0h VC / 3.0 ECTS)
          Absolvierung im 4., 5., 6. Semester empfohlen
  • Bachelorstudium Angewandte Informatik (SKZ: 511, Version: 17W.1)
    • Fach: Medieninformatik (Wahlfach)
      • 4.1 Heuristic Search ( 2.0h VC / 2.0 ECTS)
        • 621.061 Einführung in die Artificial Intelligence I (2.0h VC / 3.0 ECTS)
  • Bachelorstudium Angewandte Informatik (SKZ: 511, Version: 17W.1)
    • Fach: Natural Language Processing (Wahlfach)
      • 5.2 Heuristic Search ( 2.0h VC / 2.0 ECTS)
        • 621.061 Einführung in die Artificial Intelligence I (2.0h VC / 3.0 ECTS)
  • Bachelorstudium Angewandte Informatik (SKZ: 511, Version: 17W.1)
    • Fach: Softwareentwicklung (Wahlfach)
      • 6.2 Heuristic Search ( 2.0h VC / 2.0 ECTS)
        • 621.061 Einführung in die Artificial Intelligence I (2.0h VC / 3.0 ECTS)
  • Bachelorstudium Angewandte Informatik (SKZ: 511, Version: 17W.1)
    • Fach: Wirtschaftsinformatik (Wahlfach)
      • 7.2 Heuristic Search ( 2.0h VC / 2.0 ECTS)
        • 621.061 Einführung in die Artificial Intelligence I (2.0h VC / 3.0 ECTS)
  • Bachelorstudium Angewandte Informatik (SKZ: 511, Version: 12W.1)
    • Fach: Medieninformatik (Wahlfach)
      • Knowledge Engineering ( 2.0h VO / 2.0 ECTS)
        • 621.061 Einführung in die Artificial Intelligence I (2.0h VC / 3.0 ECTS)
  • Bachelorstudium Angewandte Informatik (SKZ: 511, Version: 12W.1)
    • Fach: Natural Language Processing (Wahlfach)
      • Knowledge Engineering ( 2.0h VO / 2.0 ECTS)
        • 621.061 Einführung in die Artificial Intelligence I (2.0h VC / 3.0 ECTS)
  • Bachelorstudium Angewandte Informatik (SKZ: 511, Version: 12W.1)
    • Fach: Softwareentwicklung (Wahlfach)
      • Knowledge Engineering ( 2.0h VO / 2.0 ECTS)
        • 621.061 Einführung in die Artificial Intelligence I (2.0h VC / 3.0 ECTS)
  • Bachelorstudium Angewandte Informatik (SKZ: 511, Version: 12W.1)
    • Fach: Wirtschaftsinformatik (Wahlfach)
      • Knowledge Engineering ( 2.0h VO / 2.0 ECTS)
        • 621.061 Einführung in die Artificial Intelligence I (2.0h VC / 3.0 ECTS)
  • Masterstudium Angewandte Informatik (SKZ: 911, Version: 13W.1)
    • Fach: Vertiefung Informatik (Pflichtfach)
      • Knowledge Engineering ( 2.0h VO / 2.0 ECTS)
        • 621.061 Einführung in die Artificial Intelligence I (2.0h VC / 3.0 ECTS)
  • Masterstudium Mathematics (SKZ: 401, Version: 18W.1)
    • Fach: Informatics (Wahlfach)
      • 8.5 Heuristic Search ( 2.0h VC / 2.0 ECTS)
        • 621.061 Einführung in die Artificial Intelligence I (2.0h VC / 3.0 ECTS)

Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung

Wintersemester 2019/20
  • 621.061 VC Einführung in die Artificial Intelligence I (2.0h / 3.0ECTS)