621.063 (24S) Introduction to Artificial Intelligence 1 - Group B
Überblick
- Lehrende/r
- LV-Titel englisch Introduction to Artificial Intelligence 1 - Group B
- LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
- LV-Modell Präsenzlehrveranstaltung
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 3.0
- Anmeldungen 36 (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
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
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
Minitests and written final exam
Prüfungsinhalt/e
- Uninformed and informed search methods
- Overview of incomplete (local) approaches to solving hard problems
- Knowledge representation and reasoning with constraint programming
- MiniZinc programming language
- Game playing
Beurteilungskriterien/-maßstäbe
- The final grade is positive if and only if:
- >= 50% of all mini test points
- >= 50 % in the written exam
Prüfungsmethode/n
Minitests and written final exam
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
|
Beurteilungsschema
Note BenotungsschemaPosition 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)
- 621.063 Introduction to Artificial Intelligence 1 - Group B (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 Wirtschaftsinformatik
(SKZ: 522, Version: 20W.2)
-
Fach: Spezialisierung Angewandte Informatik
(Wahlfach)
-
Spezialisierung Angewandte Informatik (
0.0h VO, VC, KS, UE / 6.0 ECTS)
- 621.063 Introduction to Artificial Intelligence 1 - Group B (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)
- 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)
- 621.063 Introduction to Artificial Intelligence 1 - Group B (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
- 621.061 VC Introduction to Artificial Intelligence 1 - Group A (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)