Syllabus

Title
2196 Machine Learning and the MNC
Instructors
Mag. Claus Aichinger, B.Sc., Univ.Prof. Thomas Lindner, Ph.D.
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
09/11/19 to 09/23/19
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Dates
Day Date Time Room
Monday 10/07/19 05:00 PM - 08:00 PM TC.3.10
Monday 10/14/19 05:00 PM - 08:00 PM D1.1.074
Monday 10/21/19 05:00 PM - 08:00 PM D1.1.074
Monday 10/28/19 05:00 PM - 08:00 PM D1.1.074
Monday 11/04/19 05:00 PM - 08:00 PM D1.1.074
Friday 11/15/19 04:00 PM - 07:00 PM TC.2.03
Monday 11/25/19 05:00 PM - 08:00 PM D1.1.074
Monday 12/02/19 05:00 PM - 08:00 PM D1.1.074
Contents

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

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.

Attendance requirements

Attendance at all sessions is required.

Teaching/learning method(s)

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/657176. The Google Colaboratory environment for this course is available at http://tiny.cc/mncml.

Assessment

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

Readings
1 Author: Aurélien Géron
Title:

Hands-on machine larning with Scikit-Learn & TensorFlow


Publisher: O'Reilly
Edition: 8
Year: 2018
Content relevant for class examination: No
Content relevant for diploma examination: No
Recommendation: Reference literature
Type: Book
2 Author: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Title:

An Introduction to Statistical Learning


Publisher: Springer
Edition: 8
Year: 2017
Content relevant for class examination: No
Content relevant for diploma examination: No
Recommendation: Reference literature
Type: Book
Recommended previous knowledge and skills

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.

Last edited: 2019-09-10



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