623.131 (11S) Selected Topics in Artificial Intelligence

Sommersemester 2011

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Erster Termin der LV
06.06.2011 10:00 - 12:00 HS 10 On Campus
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Überblick

Lehrende/r
LV-Titel englisch
LV-Art
Vorlesung-Kurs (prüfungsimmanente LV )
Semesterstunde/n
2.0
ECTS-Anrechnungspunkte
3.0
Anmeldungen
11
Organisationseinheit
Unterrichtssprache
Englisch
LV-Beginn
06.06.2011
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 exam

Inhalt/e

Advanced heuristic search methods

Themen

  • Machine learning from noisy data
  • Language processing with DCG grammars
  • Bayesian Networks
  • Qualitative Modeling and Reasoning

Schlagworte

Heuristic search, Machine learning, Bayesian Networks, Artificial Intelligence

Lehrziel

Introduce some particularly useful and interesting methods of Artificial Intelligence Develop understanding of these techniques sufficient for application in practice

Erwartete Vorkenntnisse

Basics of computer requirements Knowledge of some basics of Artificial Intelligence Basic knowledge of Prolog is helpful

Literatur

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 exam

Inhalt/e

Advanced heuristic search methods

Themen

  • Machine learning from noisy data
  • Language processing with DCG grammars
  • Bayesian Networks
  • Qualitative Modeling and Reasoning

Schlagworte

Heuristic search, Machine learning, Bayesian Networks, Artificial Intelligence

Lehrziel

Introduce some particularly useful and interesting methods of Artificial Intelligence Develop understanding of these techniques sufficient for application in practice

Erwartete Vorkenntnisse

Basics of computer requirements Knowledge of some basics of Artificial Intelligence Basic knowledge of Prolog is helpful

Literatur

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

written

Beurteilungsschema

Note Benotungsschema

Position im Curriculum

  • Lehramtsstudium Unterrichtsfach Informatik und Informatikmanagement (SKZ: 884, Version: 04W.7)
    • 2.Abschnitt
      • Fach: Angewandte Informatik (LI 2.3) (Pflichtfach)
        • Ausgewählte Kapitel aus Artificial Intelligence ( 2.0h VK / 4.0 ECTS)
          • 623.131 Selected Topics in Artificial Intelligence (2.0h VK / 3.0 ECTS)
  • Masterstudium Informatik (SKZ: 921, Version: 09W.1)
    • 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)
  • Masterstudium Informatik (SKZ: 921, Version: 09W.1)
    • Fach: Intelligent Information Systems in Production, Operation and Management (POM) (Pflichtfach)
      • Selected Topics in Intelligent Systems ( 2.0h VK / 4.0 ECTS)
        • 623.131 Selected Topics in Artificial Intelligence (2.0h VK / 4.0 ECTS)
  • Masterstudium Informationsmanagement (SKZ: 922, Version: 05W.2)
    • 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)
  • 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)

Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung

Sommersemester 2021
  • 626.017 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
Sommersemester 2020
  • 626.017 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
Sommersemester 2019
  • 623.131 VC Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
Sommersemester 2018
  • 623.131 VC Selected Topics in Machine Learning (2.0h / 4.0ECTS)
Sommersemester 2017
  • 623.131 VC Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
Sommersemester 2016
  • 623.131 VC Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
Sommersemester 2015
  • 623.131 VK Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
Sommersemester 2014
  • 623.131 VK Selected Topics in Artificial Intelligence (2.0h / 4.0ECTS)
Sommersemester 2013
  • 623.131 VK Selected Topics in Artificial Intelligence (2.0h / 3.0ECTS)
Sommersemester 2012
  • 623.131 VK Selected Topics in Artificial Intelligence (2.0h / 3.0ECTS)