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