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Fundamentals of Machine Learning for Executives
Last Updated: 2026-02-05 16:23:39
Abstract
Machine Learning is a subfield of Artificial Intelligence based on the idea that algorithms can learn from data, recognize patterns, and make predictions. Business leaders don’t need to code themselves, but must understand the principles and limitations of ML to make informed strategy decisions. The course teaches concepts of ML, provides minimum coding examples and demonstrates ML technologies.
Objective
Although open to all interested MAS students, this course is specifically designed to bring participants to the subsequent ‘AI for Executives’ core course to a basic understanding of how Machine Learning works, what it can do for businesses, and how companies can develop their own ML algorithms. Without this prior knowledge, participants to the ‘AI for Executives’ course may struggle to achieve the intended learning objectives. Who should join: participants to the ‘AI for Executives’ course who have neither working knowledge of ML, nor practical experience with developing ML algorithms. Who might not benefit from this course: professional data scientists and ML engineers, or anyone with deep prior exposure to ML engineering in a business or academic setting. Learning Objectives: Participants will • understand, how ML works in theory and practice o Basic concepts of ML o Minimum Coding Examples in R o Automated/Augmenting ML technologies • learn, what ML can do and can’t do o Limitations on questions that can be answered o Performance metrics • understand, how to put ML in practice in a business context / company (Code vs. Lo/NoCode vs. hybrid decision) The students should benefit from the interaction between lecturers and students as well as from the interaction between the students themselves. During the lectures attention will be paid to regular activation, for example by exercises, by surveys or by reflection on the content.
Content
Strongly recommended for MAS students • who take “AI for Executives” in the coming semester AND • who don’t have a background in Machine Learning / Data Science Day 1 (full day): Introduction, basic concepts and exercises with classification and Regression algorithms - Taxonomy of Machine Learning algorithms - Classification (Decision Trees, Neural Networks) o Includes in-class exercises with minimum coding examples in R - Regression (Linear Regression, Neural Networks) o Includes in-class exercises with minimum coding examples in R Day 2 (half day): Basic concepts of ML algorithms (continued) - Clustering (K-means, DBSCAN) - Time Series - Natural Language Processing - Day 3 (full day): Modern ML development platforms (lo-code/no-code) - LoCode/NoCode live demos with state-of-the-art ML development software and technology platforms - Guided exercises with open-source datasets - Graded project kick-off o Teams of 4-6 students, each working on a common project o Expected Project Structure (suggestion) Motivation • What question do you want to answer and why is the question relevant? Project plan • Identify the course of tasks to be performed Data • Publicly available datasets (as presented in the lecture) or own dataset (if students want) • Perform adequate data processing Analysis • prediction model that answers the question(s) linked to the chosen dataset incl. technical performance assessment Conclusion & Critique • Evaluate the answers to the question and the reliability • Discuss limitations, challenges and learnings of the team throughout this project The project can, but must not include coding. Minimum Coding examples from lectures could be adapted and re-used. Day 4 (half day): Graded projects review and discussion - Each project team to pitch their project’s findings and conclusions to the class: o 10 mins presentation per project/team o 10 mins discussion per project/team - Course Wrap-up
General Information
- Language
- English
- Levels
- NDS
- Frequency
- Yearly recurring
Examination
- Type
- ungraded semester performance
Registration & Places
- Max Places
- 70
- Signup End
- 15.01.2023
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| seminar |
Fundamentals of Machine Learning for Executives
Four-day course.
Monday and Friday: 08:30-17:00; Saturday 13:15-16:45
|
|
24 h semesterly |