2196 Machine Learning and the MNC
Mag. Claus Aichinger, B.Sc., Thomas Lindner, PhD,MIM(CEMS),BSc
  • LV-Typ
  • Semesterstunden
  • Unterrichtssprache
11.09.2019 bis 23.09.2019
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Master
Wochentag Datum Uhrzeit Raum
Montag 07.10.2019 17:00 - 20:00 TC.3.10
Montag 14.10.2019 17:00 - 20:00 D1.1.074
Montag 21.10.2019 17:00 - 20:00 D1.1.074
Montag 28.10.2019 17:00 - 20:00 D1.1.074
Montag 04.11.2019 17:00 - 20:00 D1.1.074
Freitag 15.11.2019 16:00 - 19:00 TC.2.03
Montag 25.11.2019 17:00 - 20:00 D1.1.074
Montag 02.12.2019 17:00 - 20:00 D1.1.074

Inhalte der LV

The course follows the below structure:

1. Introduction to the class and machine learning with Python

2. Conceptual foundations of machine learning

3. Business case and machine learning focus 1: Business analytics

4. Business case and machine learning focus 2: Text mining

5. Machine learning in the MNC: Recruit Japan and Vodafone (case studies)

6. Discussion session digital technologies: Artificial intelligence, machine learning, quantum computing, and the blockchain (this is a joint session together with class 2194)

7. Coaching session for group projects

8. Group project presentations and feedback

Lernergebnisse (Learning Outcomes)

In this class, students will learn how to apply machine learning to prominent business processes in Multinational Corporations (MNCs). The class introduces students to machine learning techniques in Python, which help MNCs reduce complex information to manageable outputs. Students are expected to have a basic understanding of statistics, for example evidenced by successful completion of the courses “Managing and Analyzing Data for Business Decisions”, “Data Science for International Business”, or similar courses in other study programs. Alternatively, students can familiarize themselves with basic statistical concepts using self-teaching materials provided before the course starts. Although helpful, no prior experience with programming languages is required.

Regelung zur Anwesenheit

Attendance at all sessions is required.


The class is a workshop-style course, with many interactive elements. Students are expected to give presentations, provide feedback on each other’s work, and discuss their progress with instructors. The case study package for session 5 is available here: The Google Colaboratory environment for this course is available at

Leistung(en) für eine Beurteilung

40% class paper (to be delivered three weeks after the final session)

15% final presentation

15% peer rating

10% class participation

10% pre-class tests between sessions

10% peer feedback on final presentation


1 Autor/in: Aurélien Géron

Hands-on machine larning with Scikit-Learn & TensorFlow

Verlag: O'Reilly
Auflage: 8
Jahr: 2018
Prüfungsstoff: Nein
Diplomprüfungsstoff: Nein
Empfehlung: Referenzliteratur
Art: Buch
2 Autor/in: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

An Introduction to Statistical Learning

Verlag: Springer
Auflage: 8
Jahr: 2017
Prüfungsstoff: Nein
Diplomprüfungsstoff: Nein
Empfehlung: Referenzliteratur
Art: Buch

Empfohlene inhaltliche Vorkenntnisse

Students are expected to prepare chapters 1 of "Hands-on Machine Learning" (you can skip the parts on "Reinforcement Learning" and "Batch and Online Learning") and chapters 1 and 2.1 of "An Introduction to Statistical Learning" before the first session.

Zuletzt bearbeitet: 10.09.2019