621.061 (24S) Introduction to Artificial Intelligence 1 - Group A
Überblick
- Lehrende/r
- 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
- Unterrichtssprache Englisch
- LV-Beginn 07.03.2024
- eLearning zum Moodle-Kurs
Zeit und Ort
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
Prüfungsinformationen
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 BenotungsschemaPosition 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
-
13.1 AIML1: Introduction to Artificial Intelligence I (
0.0h VC / 4.0 ECTS)
-
Fach: Minitrack 7: Artificial Intelligence and Machine Learning
(Wahlfach)
- 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
-
7.3 Einführung in die Artificial Intelligence I (
2.0h VC / 3.0 ECTS)
-
Fach: Vertiefung Informatik
(Wahlfach)
- 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)
-
4.1 Heuristic Search (
2.0h VC / 2.0 ECTS)
-
Fach: Medieninformatik
(Wahlfach)
- 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)
-
5.2 Heuristic Search (
2.0h VC / 2.0 ECTS)
-
Fach: Natural Language Processing
(Wahlfach)
- 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)
-
6.2 Heuristic Search (
2.0h VC / 2.0 ECTS)
-
Fach: Softwareentwicklung
(Wahlfach)
- 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)
-
7.2 Heuristic Search (
2.0h VC / 2.0 ECTS)
-
Fach: Wirtschaftsinformatik
(Wahlfach)
- 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
-
Spezialisierung Angewandte Informatik (
0.0h VO, VC, KS, UE / 6.0 ECTS)
-
Fach: Spezialisierung Angewandte Informatik
(Wahlfach)
- 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)
-
8.5 Heuristic Search (
2.0h VC / 2.0 ECTS)
-
Fach: Informatics
(Wahlfach)
- 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)
-
4.1 Introduction to Artificial Intelligence I (
2.0h VC / 3.0 ECTS)
-
Fach: Artificial Intelligence
(Pflichtfach)
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
-
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)