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Leveraging Generative AI for Sustainable Business Value
Last Updated: 2026-06-03 00:07:53
Abstract
Machine learning has revolutionized various domains across industry sectors. Advances in GenAI has triggered this development and has created additional fantasies for future applications. Hence, an understanding its practical applications is crucial for professionals in today’s data-driven world. This course delves into the concepts of ML, its applications and use cases and ethical considerations.
Objective
After taking this course, participants will - Understand the fundamental concepts of ML with some basic hands-on cases - Understand and reflect on the ethical implications of ML algorithms, discuss bias, fairness, and transparency in AI systems. - Understand the concepts behind advances in deep learning and reinforcement learning, transfomers - Learn about applications of deep learning and reinforcement learning in finance - Learn about key areas of AI in robotics, like computer vision, imitation learning, planning, robot control - Get an overview of deep learning in different industries like logistics, automobile, healthcare - Learn about the power and limits of LLMs - Learn about prompt engineering, fine tuning and working with LLMs
Content
Advancements in artificial intelligence (AI) have opened up exciting opportunities across various domains. In this lecture we explore the potential and hurdles in four key areas: machine learning, deep learning, reinforcement learning, and language models across different applications, with a focus on finance and robotics. Day 1: Introduction to Machine Learning, Transparency, Interpretability and ethical aspects of ML Day 2: Introduction to Deep Learning, Reinforcement Learning, Transformers, applications in finance and robotics, overview of deep learning across industries Day 3: Focus on Large Language Models with Applications from prompt engineering and working with large language models in a business context By the end of this course, students will have a comprehensive understanding of machine learning, its ethical dimensions, and practical applications. The course is held in a workshop format with lecture and group work elements. Active participation on all course days is mandatory. Participants will work in groups on selected cases and have the opportunity to follow some basic coding examples in a Jupiter Notebook. Programming skills are not mandatory. An understanding of basic machine learning concepts is welcomed but also not mandatory (e.g. you took the class “Fundamentals on ML for Executives” or “AI for Executives”). In the beginning of the course, we will do a short primer on mathematics and statistics and some fundamental aspects of machine learning to bring all students on the same level. Grading (ungraded semester performance) is based on active participation in the class and a short written report (ungraded) after the course.
General Information
- Language
- English
- Levels
- NDS
- Frequency
- Yearly recurring
Examination
- Type
- ungraded semester performance
Registration & Places
- Max Places
- 40
- Signup End
- 02.08.2026
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| seminar |
Leveraging Generative AI for Sustainable Business Value
Three-day course.
Thursday and Friday: 09:15-17:00; Saturday: 09:15-16:45.
|
No time listed | 24 h semesterly |
Offered In
-
MAS in Management, Technology, and Economics (MAS MTEC Onboarding Workshop for 1st Semester Students: Friday, 11.09.2026, 09.00 -17.30, LEE E 101)