623.613 (19S) Applied Machine Learning

Sommersemester 2019

Anmeldefrist abgelaufen.

Erster Termin der LV
06.03.2019 12:00 - 14:00 , S.1.42
Nächster Termin:
26.06.2019 12:00 - 14:00 , S.1.42

Überblick

Lehrende/r
LV-Titel englisch
Applied Machine Learning
LV-Art
Vorlesung-Kurs (prüfungsimmanente LV )
Semesterstunde/n
2.0
ECTS-Anrechungspunkte
4.0
Anmeldungen
19 (25 max.)
Organisationseinheit
Unterrichtssprache
Englisch
mögliche Sprache/n der Leistungserbringung
Deutsch
LV-Beginn
06.03.2019
eLearning
zum Moodle-Kurs
Studienberechtigungsprüfung
Ja
Seniorstudium Liberale
Ja

LV-Beschreibung

Intendierte Lernergebnisse

The course provides a practical introduction into machine learning methods applied in computer science.

Please consider visiting: 

  • "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.

Lehrmethodik

Lectures with a student's project applying machine learning.

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 keine Anmeldevoraussetzung

The basics of the probability theory given in, e.g.,  "Uncertain Knowledge" (621.065), Stochastik 1/2, etc. 

Knowledge of R and/or Python is a plus.

Please consider visiting: 

  • "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.

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

  • 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)
        • 623.613 Applied Machine Learning (2.0h VC / 4.0 ECTS)
  • Masterstudium Angewandte Informatik (SKZ: 911, Version: 13W.1)
    • Fach: Knowledge and Data Engineering (Wahlfach)
      • Knowledge Representation and Reasoning for Games ( 2.0h VK / 4.0 ECTS)
        • 623.613 Applied Machine Learning (2.0h VC / 4.0 ECTS)

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