623.616 (19W) Topics in Knowledge and Data Engineering for Business Information Systems

Wintersemester 2019/20

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Erster Termin der LV
02.10.2019 14:00 - 16:00 S.2.42 On Campus
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Überblick

Lehrende/r
LV-Titel englisch Topics in Knowledge and Data Engineering for Business Information Systems
LV-Art Vorlesung-Kurs (prüfungsimmanente LV )
Semesterstunde/n 2.0
ECTS-Anrechnungspunkte 4.0
Anmeldungen 20 (30 max.)
Organisationseinheit
Unterrichtssprache Englisch
mögliche Sprache/n der Leistungserbringung Deutsch
LV-Beginn 02.10.2019
eLearning zum Moodle-Kurs
Seniorstudium Liberale Ja

Zeit und Ort

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LV-Beschreibung

Intendierte Lernergebnisse

The course provides a practical introduction into machine learning methods applied in computer science using Python programming language.

Lehrmethodik inkl. Einsatz von eLearning-Tools

The course combines a set of lectures with home work and a project in which students demonstrate their knowledge of machine learning techniques by applying them to solve some practical problem.

Inhalt/e

  • Introduction to machine learning
  • Supervised learning: classification and regression
  • Unsupervised learning: transformation of data and clustering
  • Validation of models
  • Overview of reinforcement learning

Erwartete Vorkenntnisse

Basics of the probability theory as well as knowledge of R and/or Python is a plus.

After/during the course, please consider visiting also courses providing an in-depth discussion of machine learning techniques: 

  • "Selected Topics in Artificial Intelligence" (623.131) for an in-depth review of reinforcement learning methods,
  • "Selected Topics in Knowledge and Data Engineering: Data Mining" (623.253) for an overview of data acquisition and manipulation methods.
  • "Current Topics in Distributed Multimedia Systems: Video Analysis & Retrieval" (623.915) which among other interesting topics considers various architectures and applications of Deep Neural Networks to image/video processing and recognition

Literatur

Beginners:

  • James, G., Witten, D., & Hastie, T. (2014). An Introduction to Statistical Learning: With Applications in R. Springer
  • Raschka, S. (2015). Python machine learning. Packt Publishing Ltd.
  • Wickham, H. and Grolemund G. (2017) R for Data Science. O'Reilly Media Inc. 

Classics:

  • Mitchell, T. (1997) Machine Learning. McGraw Hill.
  • Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
  • Friedman, J., Hastie, T., & Tibshirani, R. (2009). The elements of statistical learning.  2nd edition, Springer.


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.

Beurteilungsschema

Note Benotungsschema

Position im Curriculum

  • Bachelorstudium Angewandte Informatik (SKZ: 511, Version: 19W.2)
    • Fach: Artificial Intelligence (Wahlfach)
      • 8.1 Artificial Intelligence ( 0.0h XX / 12.0 ECTS)
        • 623.616 Topics in Knowledge and Data Engineering for Business Information Systems (2.0h VC / 4.0 ECTS)
          Absolvierung im 4., 5., 6. Semester empfohlen
  • Masterstudium Angewandte Informatik (SKZ: 911, Version: 13W.1)
    • Fach: Business Information Systems (Wahlfach)
      • Topics in Knowledge and Data Engineering for Business Information Systems ( 2.0h VK / 4.0 ECTS)
        • 623.616 Topics in Knowledge and Data Engineering for Business Information Systems (2.0h VC / 4.0 ECTS)
  • Masterstudium Informatics (SKZ: 911, Version: 19W.2)
    • Fach: Business Information Systems (Wahlfach)
      • Weitere LVen aus dem gewählten Spezialisierungsfach ( 0.0h XX / 12.0 ECTS)
        • 623.616 Topics in Knowledge and Data Engineering for Business Information Systems (2.0h VC / 4.0 ECTS)
          Absolvierung im 1., 2. Semester empfohlen
  • Masterstudium Information Management (SKZ: 922, Version: 19W.1)
    • Fach: Information and IT Management (Pflichtfach)
      • 3.3 Current Topics in Information and IT Management ( 0.0h VC, KS, SE / 4.0 ECTS)
        • 623.616 Topics in Knowledge and Data Engineering for Business Information Systems (2.0h VC / 4.0 ECTS)
          Absolvierung im 1., 2., 3. Semester empfohlen
  • Masterstudium Informationsmanagement (SKZ: 922, Version: 13W.2)
    • Fach: Informations- und IT- Management (Pflichtfach)
      • 3.8 Current Topics in Information Management ( 2.0h SE/VC/KS / 4.0 ECTS)
        • 623.616 Topics in Knowledge and Data Engineering for Business Information Systems (2.0h VC / 4.0 ECTS)

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