621.065 (23W) Introduction to Artificial Intelligence 1 - Group C

Wintersemester 2023/24

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Erster Termin der LV
11.10.2023 12:00 - 14:00 N.0.43 On Campus
... keine weiteren Termine bekannt

Überblick

Lehrende/r
LV-Titel englisch Introduction to Artificial Intelligence 1 - Group C
LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
LV-Modell Blended-Learning-Lehrveranstaltung
Online-Anteil 30%
Semesterstunde/n 2.0
ECTS-Anrechnungspunkte 3.0
Anmeldungen 17 (30 max.)
Organisationseinheit
Unterrichtssprache Englisch
LV-Beginn 11.10.2023
eLearning zum Moodle-Kurs

Zeit und Ort

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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 inkl. Einsatz von eLearning-Tools

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

Curriculare Anmeldevoraussetzungen

Nothing 

Literatur

  • Stefan Edelkamp and Stefan Schrödl: Heuristic search: theory and applications. Elsevier, 2011
  • Rina Dechter: Constraint Processing. Morgan Kaufmann Publishers, 2003
  • Stefan Edelkamp and Stefan Schrödl: Heuristic search: theory and applications. Elsevier, 2012

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 inkl. Einsatz von eLearning-Tools

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

Curriculare Anmeldevoraussetzungen

Nothing

Literatur

  • Stefan Edelkamp and Stefan Schrödl: Heuristic search: theory and applications. Elsevier, 2011
  • Rina Dechter: Constraint Processing. Morgan Kaufmann Publishers, 2003
  • Stefan Edelkamp and Stefan Schrödl: Heuristic search: theory and applications. Elsevier, 2012

Link auf weitere Informationen

Prüfungsinformationen

Im Fall von online durchgeführten Prüfungen sind die Standards zu beachten, die die technischen Geräte der Studierenden erfüllen müssen, um an diesen Prüfungen teilnehmen zu können.

Prüfungsmethode/n

Mini tests, written and oral examinations

Prüfungsinhalt/e

  • 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  

Beurteilungskriterien/-maßstäbe

  • The final grade is positive if and only if:
    • >= 50% of all mini test points are reached.
    • >= 40 % in the written exam are reached.
    • The oral examination is positive. 
  • Based on the oral examination, the final grading is set. 

Prüfungsmethode/n

Mini tests, written and oral examinations 

Prüfungsinhalt/e

  • 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   

Beurteilungskriterien/-maßstäbe

  • The final grade is positive if and only if:
    • >= 50% of all mini test points are reached.
    • >= 40 % in the written exam are reached.
    • The oral examination is positive. 
  • Based on the oral examination, the final grading is set. 

Beurteilungsschema

Note Benotungsschema

Position im Curriculum

  • Bachelorstudium Angewandte Informatik (SKZ: 511, Version: 19W.2)
    • Fach: Vertiefung Informatik (Wahlfach)
      • 7.3 Einführung in die Artificial Intelligence I ( 2.0h VC / 3.0 ECTS)
        • 621.065 Introduction to Artificial Intelligence 1 - Group C (2.0h VC / 3.0 ECTS)
          Absolvierung im 4., 5., 6. Semester empfohlen
  • Bachelorstudium Wirtschaftsinformatik (SKZ: 522, Version: 20W.2)
    • Fach: Spezialisierung Angewandte Informatik (Wahlfach)
      • Spezialisierung Angewandte Informatik ( 0.0h VO, VC, KS, UE / 6.0 ECTS)
        • 621.065 Introduction to Artificial Intelligence 1 - Group C (2.0h VC / 3.0 ECTS)
          Absolvierung im 6. Semester empfohlen
  • Bachelorstudium Robotics and Artificial Intelligence (SKZ: 295, Version: 22W.1)
    • Fach: Artificial Intelligence (Pflichtfach)
      • 4.1 Introduction to Artificial Intelligence I ( 2.0h VC / 3.0 ECTS)
        • 621.065 Introduction to Artificial Intelligence 1 - Group C (2.0h VC / 3.0 ECTS)

Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung

Sommersemester 2024
  • 621.061 VC Introduction to Artificial Intelligence 1 - Group A (2.0h / 3.0ECTS)
  • 621.063 VC Introduction to Artificial Intelligence 1 - Group B (2.0h / 3.0ECTS)
Wintersemester 2023/24
  • 621.061 VC Introduction to Artificial Intelligence 1 - Group A (2.0h / 3.0ECTS)
  • 621.063 VC Introduction to Artificial Intelligence 1 - Group B (2.0h / 3.0ECTS)
Sommersemester 2023
  • 621.061 VC Einführung in die Artificial Intelligence I (2.0h / 3.0ECTS)
Wintersemester 2022/23
  • 621.061 VC Einführung in die Artificial Intelligence I (2.0h / 3.0ECTS)
Sommersemester 2022
  • 621.061 VC Einführung in die Artificial Intelligence I (2.0h / 3.0ECTS)
Wintersemester 2021/22
  • 621.061 VC Einführung in die Artificial Intelligence I (2.0h / 3.0ECTS)
Sommersemester 2021
  • 621.061 VC Einführung in die Artificial Intelligence I (2.0h / 3.0ECTS)
Wintersemester 2020/21
  • 621.061 VC Einführung in die Artificial Intelligence I (2.0h / 3.0ECTS)
Sommersemester 2020
  • 621.061 VC Einführung in die Artificial Intelligence I (2.0h / 3.0ECTS)
Wintersemester 2019/20
  • 621.061 VC Einführung in die Artificial Intelligence I (2.0h / 3.0ECTS)