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

Wintersemester 2019/20

Anmeldefrist abgelaufen.

Erster Termin der LV
02.10.2019 14:00 - 16:00 , S.2.42
Nächster Termin:
20.11.2019 14:00 - 16:00 , S.2.42

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-Anrechungspunkte
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

LV-Beschreibung

Intendierte Lernergebnisse

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

Lehrmethodik

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

Beurteilungsschema

Note/Grade Benotungsschema

Position im Curriculum

  • Bachelorstudium Angewandte Informatik (SKZ: 511, Version: 19W.1)
    • 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.1)
    • 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. 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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