Executive Education


Over the last couple of years, we have seen a resurgence of interest in Machine Learning applications by researchers and practitioners alike. The most considerable breakthroughs have occurred in the subfield of Deep Learning – a collection of Machine Learning methods inspired by the workings of the human brain. Today, Deep Learning is at the heart of many state-of-the-art (predictive) analysis tools providing a competitive edge to organizations. Examples include, credit default prediction, pattern recognition, stock price prediction, sentiment analysis, outlier detection, and natural language processing, to name only a few.

The course introduces Deep Learning methods including Feed Forward Neural Networks, and its applications in the domain of NLP. Learned methods will be applied to problems such as credit default, customer churn, and stock price predictions. Against the background of recent debates and regulations concerning the “black box” nature of Deep Learning applications, the course will also touch upon interpretability methods.

Any questions?

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Amanguli Yasheng

 +49 69 798 33516


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Course Format

This course is offered in the part-time Master of Digital Transformation Management MBA program and may be attended on a “no credit” basis by individuals not enrolled in the program. Course participants are visitors who are not responsible for assignments and do not take an exam or earn academic credits. As the number of seats in the course is limited, we recommend to register online early.

Learning Objectives

Upon completion of the course, students will be able to:

  • Understand the inner workings of Deep Learning techniques.
  • Know how to handle and pre-process data in Python using popular libraries such as Pandas, NumPy, Scikit-Learn, and Tensorflow.
  • Be able to build Deep Learning models in Python using TensorFlow.
  • Apply learned AI techniques to problems outside the classroom
  • Understand limits and problems associated with contemporary AI methods
Key Concepts
  • Concept of supervised machine learning.
  • Data pre-processing steps.
  • Mathematical foundations of Deep Learning architectures.



Assist. Prof. Dr. Kevin Bauer

Kevin Bauer - Assistant Professor, “E-Business and E-Government”, Faculty of Business Administration, University of Mannheim - received his PhD in economics from Goethe University. As part of his doctoral studies, he was a visiting researcher at the University of Michigan. During his PhD, he also completed a master’s degree in information systems with a focus on artificial intelligence. Since 01.01.2023, Kevin Bauer is an Assistant Professor at the University of Mannheim. He regularly gives lectures on topics related to artificial intelligence, machine learning, blockchain and other decision technologies for European financial supervisors. Among others, Kevin previously taught courses Behavioral Economics (Goethe University), on Managerial Economics (University of Essex), and Applied Deep Learning  in Finance (Goethe Business School).

Kevin’s research focuses on the interaction between humans and artificial intelligence and the employment of machine learning systems to tackle economic problems. Current projects leverage lab and field experiments to study how explainable and causal machine learning affect human behavior and economic outcomes.

Key Facts

Course materials

Course materials will be provided in electronic form.




Campus Westend of Goethe University Frankfurt.

Certificate of participation

A GBS certificate of participation is awarded upon completion of the course.

Course Fee

€ 1,900 (fee is exempt from VAT). The fee for GBS students or alumni amounts to € 800.

Course Schedule
Date Session
Fri., January 10, 2025 13:00-15:00; 15:30-17:30; 18:00-20:00
Sat., January 11, 2025 09:00-11:00; 11:30-13:30; 14:30-16:30
Fri., February 7, 2025 13:00-15:00; 15:30-17:30; 18:00-20:00
Sat., February 8, 2025 09:00-11:00; 11:30-13:30; 14:30-16:30

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