Syllabus

Title
2140 Machine Learning Applications in Marketing
Instructors
Patrick Bachmann, MA, Dr. Markus Meierer
Contact details
  • Type
    PI
  • Weekly hours
    2
  • Language of instruction
    Englisch
Registration
09/21/18 to 09/28/18
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Dates
Day Date Time Room
Monday 10/15/18 09:00 AM - 11:30 AM D3.0.237
Monday 10/15/18 11:30 AM - 03:30 PM D4.0.022
Tuesday 10/16/18 12:00 PM - 06:30 PM TC.4.28
Wednesday 10/17/18 10:30 AM - 01:00 PM TC.2.02
Wednesday 10/17/18 01:00 PM - 04:00 PM D5.1.004
Monday 10/22/18 09:00 AM - 03:30 PM EA.5.034
Tuesday 10/23/18 09:00 AM - 03:30 PM EA.5.034
Wednesday 10/24/18 09:00 AM - 03:00 PM D2.0.374

Contents

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).

 

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.

Attendance requirements

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

Teaching/learning method(s)

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

Assessment

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

Prerequisites for participation and waiting lists

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.

Last edited: 2018-09-18



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