623.613 (17S) Applied Machine Learning

Sommersemester 2017

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Erster Termin der LV
22.05.2017 09:00 - 12:00 , E 1.37
... keine weiteren Termine bekannt

Überblick

Lehrende/r
LV-Titel englisch
Applied Machine Learning
LV-Art
Vorlesung-Kurs (prüfungsimmanente LV )
Semesterstunde/n
2.0
ECTS-Anrechungspunkte
4.0
Anmeldungen
11 (25 max.)
Organisationseinheit
Unterrichtssprache
Englisch
mögliche Sprache/n der Leistungserbringung
Deutsch
LV-Beginn
22.05.2017
eLearning
zum Moodle-Kurs

LV-Beschreibung

Intendierte Lernergebnisse

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

Note: The course appointments are blocked and are planned for May.

Lehrmethodik


The course is build on top of the "Uncertain Knowledge" (621.065) and assumes the knowledge of the presented material.

In addition, it is recommended to visit the course "Selected Topics in Artificial Intelligence" (623.131) for an in-depth review of data analysis methods as well as learning of bayesian networks, rule-sets and decision trees.

Knowledge of R and/or Python is a plus.

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

Literatur

Please also consider visiting the course "Selected Topics in Knowlegde and Data Engineering: Data Mining" (623.253) given by Prof. Morzy for an in-depth review of data acquisition and manipulation methods.


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)
  • Masterstudium Informatik (SKZ: 921, Version: 09W.1)
    • Fach: Intelligent Information Systems in Production, Operation and Management (POM) (Pflichtfach)
      • Weitere Lehrveranstaltungen aus dem Spezialisierungsfach ( 4.0h XX / 8.0 ECTS)
        • 623.613 Applied Machine Learning (2.0h VC / 4.0 ECTS)
  • Masterstudium Informatik (SKZ: 921, Version: 03W.1)
    • Fach: Intelligent Information Systems in Production, Operation and Management (POM) (Pflichtfach)
      • Weitere Lehrveranstaltungen aus dem Spezialisierungsfach ( 4.0h XX / 8.0 ECTS)
        • 623.613 Applied Machine Learning (2.0h VC / 4.0 ECTS)

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