2140 Machine Learning Applications in Marketing
Patrick Bachmann, MA, Dr. Markus Meierer
  • LV-Typ
  • Semesterstunden
  • Unterrichtssprache
21.09.2018 bis 28.09.2018
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Bachelor
Wochentag Datum Uhrzeit Raum
Montag 15.10.2018 09:00 - 11:30 D3.0.237
Montag 15.10.2018 11:30 - 15:30 D4.0.022
Dienstag 16.10.2018 12:00 - 18:30 TC.4.28
Mittwoch 17.10.2018 10:30 - 13:00 TC.2.02
Mittwoch 17.10.2018 13:00 - 16:00 D5.1.004
Montag 22.10.2018 09:00 - 15:30 EA.5.034
Dienstag 23.10.2018 09:00 - 15:30 EA.5.034
Mittwoch 24.10.2018 09:00 - 15:00 D2.0.374

Inhalte der LV

Since the amount of available data is steadily increasing, smart data analysis will become more and more important in the future. Machine learning plays a significant role in this context. This course introduces supervised machine learning techniques in a non-technical, hands-on way with integrated exercises and group works.


Among the topics to be discussed in this course are:  the general process of supervised machine learning, sampling and cross-validation techniques, model performance evaluation, ensemble techniques as well as how specific algorithms work (decision trees, random forest, logistic regression, support vector machines, neural networks/deep learning).


Lernergebnisse (Learning Outcomes)

The learning objectives of this course are as follows:

  • Get familiar with the concept and general analysis process of supervised machine learning and
  • Understand the basic theory behind various machine learning techniques.
  • Apply different machine learning techniques and evaluate their performance.

Regelung zur Anwesenheit

Due to the blocked structure and the intensive nature of the course, full attendance is required.


This course integrates various teaching methods such as interactive lectures, class discussions, exercises and group work.

Leistung(en) für eine Beurteilung

Individual evaluation based multiple-choice tests (70%), performance in online exercises (30%) and participation during lectures and exercises (bonus).

Teilnahmevoraussetzung(en) und Vergabe von Wartelistenplätzen

Basic knowledge of standard statistical software packages, such as R (or Python) are recommended. To ensure a shared set of R skills among all participants, we will provide a condensed introduction to R on the first day.

Zuletzt bearbeitet: 18.09.2018