623.131 (12S) Selected Topics in Artificial Intelligence
Überblick
- Lehrende/r
- LV-Titel englisch nichts eingestellt
- LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 3.0
- Anmeldungen 18
- Organisationseinheit
- Unterrichtssprache Englisch
- LV-Beginn 10.06.2012
- Anmerkungen Allgmeine Informatik
Zeit und Ort
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LV-Beschreibung
Lehrmethodik inkl. Einsatz von eLearning-Tools
Course comprises lectures interleaved with exercises Student's active participation is desirable and acknowledge in exam marks Students are offered to do a project in teams of up to three members, successfull project score bonus points taken into account in final examInhalt/e
Advanced heuristic search methodsThemen
- Machine learning from noisy data
- Language processing with DCG grammars
- Bayesian Networks
- Qualitative Modeling and Reasoning
Lehrziel
Introduce some particularly useful and interesting methods of Artificial Intelligence Develop understanding of these techniques sufficient for application in practiceErwartete Vorkenntnisse
Basics of computer requirements Knowledge of some basics of Artificial Intelligence Basic knowledge of Prolog is helpfulLiteratur
Bratko, Prolog Programming for Artificial Intelligence, 4th Edition, Pearson Education, March 2011 (3rd edition of this book is also adequate) Additional: S. Russell, P. Norvig, Artificial Intelligence: a Modern Approach, 3rd. edition, Prentice-HAll, 2009. I.H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd edition. Elsevier, 2005.Lehrmethodik inkl. Einsatz von eLearning-Tools
Course comprises lectures interleaved with exercises Student's active participation is desirable and acknowledge in exam marks Students are offered to do a project in teams of up to three members, successfull project score bonus points taken into account in final examInhalt/e
Advanced heuristic search methodsThemen
- Machine learning from noisy data
- Language processing with DCG grammars
- Bayesian Networks
- Qualitative Modeling and Reasoning
Lehrziel
Introduce some particularly useful and interesting methods of Artificial Intelligence Develop understanding of these techniques sufficient for application in practiceErwartete Vorkenntnisse
Basics of computer requirements Knowledge of some basics of Artificial Intelligence Basic knowledge of Prolog is helpfulLiteratur
Bratko, Prolog Programming for Artificial Intelligence, 4th Edition, Pearson Education, March 2011 (3rd edition of this book is also adequate) Additional: S. Russell, P. Norvig, Artificial Intelligence: a Modern Approach, 3rd. edition, Prentice-HAll, 2009. I.H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd edition. Elsevier, 2005.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.
Beurteilungskriterien/-maßstäbe
schriftlich
Beurteilungskriterien/-maßstäbe
writtenBeurteilungsschema
Note BenotungsschemaPosition im Curriculum
- Diplom-Lehramtsstudium Unterrichtsfach Informatik und Informatikmanagement
(SKZ: 884, Version: 04W.7)
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2.Abschnitt
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Fach: Angewandte Informatik (LI 2.3)
(Pflichtfach)
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Ausgewählte Kapitel aus Artificial Intelligence (
2.0h VK / 4.0 ECTS)
- 623.131 Selected Topics in Artificial Intelligence (2.0h VK / 3.0 ECTS)
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Ausgewählte Kapitel aus Artificial Intelligence (
2.0h VK / 4.0 ECTS)
-
Fach: Angewandte Informatik (LI 2.3)
(Pflichtfach)
-
2.Abschnitt
- Masterstudium Informatik
(SKZ: 921, Version: 09W.1)
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Fach: Data and Knowledge Engineering
(Pflichtfach)
-
Artificial Intelligence (
2.0h VK / 4.0 ECTS)
- 623.131 Selected Topics in Artificial Intelligence (2.0h VK / 4.0 ECTS)
-
Artificial Intelligence (
2.0h VK / 4.0 ECTS)
-
Fach: Data and Knowledge Engineering
(Pflichtfach)
- Masterstudium Informatik
(SKZ: 921, Version: 09W.1)
-
Fach: Intelligent Information Systems in Production, Operation and Management (POM)
(Pflichtfach)
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Selected Topics in Intelligent Systems (
2.0h VK / 4.0 ECTS)
- 623.131 Selected Topics in Artificial Intelligence (2.0h VK / 4.0 ECTS)
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Selected Topics in Intelligent Systems (
2.0h VK / 4.0 ECTS)
-
Fach: Intelligent Information Systems in Production, Operation and Management (POM)
(Pflichtfach)
- Masterstudium Informationsmanagement
(SKZ: 922, Version: 05W.2)
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Fach: Data and Knowledge Engineering
(Wahlfach)
-
Spezialgebiete des Informationsmanagements (
4.0h VO, VK / 6.0 ECTS)
- 623.131 Selected Topics in Artificial Intelligence (2.0h VK / 3.0 ECTS)
-
Spezialgebiete des Informationsmanagements (
4.0h VO, VK / 6.0 ECTS)
-
Fach: Data and Knowledge Engineering
(Wahlfach)
- Masterstudium Informationsmanagement
(SKZ: 922, Version: 05W.2)
-
Fach: Intelligent Information Systems in Production, Operation and Management
(Wahlfach)
-
Spezialgebiete des Informationsmanagements (
4.0h VO, VK / 6.0 ECTS)
- 623.131 Selected Topics in Artificial Intelligence (2.0h VK / 3.0 ECTS)
-
Spezialgebiete des Informationsmanagements (
4.0h VO, VK / 6.0 ECTS)
-
Fach: Intelligent Information Systems in Production, Operation and Management
(Wahlfach)
Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung
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Sommersemester 2024
- 626.017 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
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Sommersemester 2023
- 626.017 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
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Sommersemester 2022
- 626.017 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
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Sommersemester 2021
- 626.017 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
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Sommersemester 2020
- 626.017 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
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Sommersemester 2019
- 623.131 VC Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
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Sommersemester 2018
- 623.131 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
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Sommersemester 2017
- 623.131 VC Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
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Sommersemester 2016
- 623.131 VC Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
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Sommersemester 2015
- 623.131 VK Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
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Sommersemester 2014
- 623.131 VK Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
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Sommersemester 2013
- 623.131 VK Selected Topics in Artificial Intelligence (2.0h / 3.0ECTS)
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Sommersemester 2011
- 623.131 VK Selected Topics in Artificial Intelligence (2.0h / 3.0ECTS)