Syllabus

Titel
2140 Machine Learning Applications in Marketing
LV-Leiter/innen
Patrick Bachmann, MA, Dr. Markus Meierer
Kontakt
  • LV-Typ
    PI
  • Semesterstunden
    2
  • Unterrichtssprache
    Englisch
Anmeldung
21.09.2018 bis 28.09.2018
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Bachelor
Termine
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.

Lehr-/Lerndesign

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



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