Syllabus

Titel
1897 Machine Learning and the MNC
LV-Leiter/innen
Mag. Claus Aichinger, B.Sc., Thomas Lindner, PhD,MIM(CEMS),BSc
Kontakt
  • LV-Typ
    PI
  • Semesterstunden
    2
  • Unterrichtssprache
    Englisch
Anmeldung
11.09.2020 bis 28.09.2020
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Master
Termine
Wochentag Datum Uhrzeit Raum
Montag 05.10.2020 17:00 - 20:00 TC.5.03
Montag 12.10.2020 17:00 - 20:00 TC.5.03
Montag 19.10.2020 17:00 - 20:00 D5.0.002
Montag 02.11.2020 17:00 - 20:00 TC.5.01
Montag 09.11.2020 17:00 - 20:00 TC.5.03
Freitag 20.11.2020 16:00 - 19:00 Online-Einheit
Montag 30.11.2020 17:00 - 20:00 Online-Einheit
Montag 14.12.2020 17:00 - 20:00 Online-Einheit

Ablauf der LV bei eingeschränktem Campusbetrieb

If there are restrictions in place in the winter term that make regular sessions for this class impossible because of Covid-19, this class will be taught using a hybrid format. In the hybrid format, students will be able to joint the class either physically or through a digital link. Course contents and grading remain unchanged.

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: Business analytics 1

4. Business case and machine learning focus: Business analytics 2

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 1895)

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.

Lehr-/Lerndesign

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: https://hbsp.harvard.edu/import/764450. The Google Colaboratory environment for this course is available at https://short.wu.ac.at/MLMNC.

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

Literatur

1 Autor/in: Aurélien Géron
Titel:

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
Titel:

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: 30.09.2020



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