621.061 (24S) Introduction to Artificial Intelligence 1 - Group A

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Sommersemester 2024

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Erster Termin der LV
07.03.2024 13:30 - 15:00 On Campus

Nächster Termin:
02.05.2024 13:30 - 15:00 On Campus

Überblick

Lehrende/r

O.Univ.-Prof. Dipl.-Ing. Dr. Gerhard Friedrich

LV-Titel englisch

Introduction to Artificial Intelligence I - Group A

LV-Art

Vorlesung-Kurs (prüfungsimmanente LV )

LV-Modell

Blended-Learning-Lehrveranstaltung

Online-Anteil

30%

Semesterstunde/n

2.0

ECTS-Anrechnungspunkte

3.0

Anmeldungen

42 (30 max.)

Organisationseinheit

Institut für Artificial Intelligence und Cybersecurity

Unterrichtssprache

Englisch

LV-Beginn

07.03.2024

eLearning

zum Moodle-Kurs

Zeit und Ort

Liste der Termine wird geladen...

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

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

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.

Deutsch
Englisch

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

Masterstudium Management, Economics, and Data Science (SKZ: 946, Version: 23W.1)
- Fach: Minitrack 7: Artificial Intelligence and Machine Learning (Wahlfach)
  - 13.1 AIML1: Introduction to Artificial Intelligence I ( 0.0h VC / 4.0 ECTS)
    - 621.061 Introduction to Artificial Intelligence 1 - Group A (2.0h VC / 3.0 ECTS)
      Absolvierung im 2-4. Semester empfohlen

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)
    - 621.061 Introduction to Artificial Intelligence 1 - Group A (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 Introduction to Artificial Intelligence 1 - Group A (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 Introduction to Artificial Intelligence 1 - Group A (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 Introduction to Artificial Intelligence 1 - Group A (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 Introduction to Artificial Intelligence 1 - Group A (2.0h VC / 3.0 ECTS)

Bachelorstudium Wirtschaftsinformatik (SKZ: 522, Version: 20W.2)
- Fach: Spezialisierung Angewandte Informatik (Wahlfach)
  - Spezialisierung Angewandte Informatik ( 0.0h VO, VC, KS, UE / 6.0 ECTS)
    - 621.061 Introduction to Artificial Intelligence 1 - Group A (2.0h VC / 3.0 ECTS)
      Absolvierung im 6. Semester empfohlen

Masterstudium Mathematics (SKZ: 401, Version: 18W.1)
- Fach: Informatics (Wahlfach)
  - 8.5 Heuristic Search ( 2.0h VC / 2.0 ECTS)
    - 621.061 Introduction to Artificial Intelligence 1 - Group A (2.0h VC / 2.0 ECTS)

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)
    - 621.061 Introduction to Artificial Intelligence 1 - Group A (2.0h VC / 3.0 ECTS)

Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung

Sommersemester 2024

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)
621.065 VC Introduction to Artificial Intelligence 1 - Group C (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)