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, especially in the financial industry. Examples include credit default prediction, pattern recognition, stock price prediction, sentiment analyses, outlier detection, and natural language processing, to name only a few. The course introduces Deep Learning methods, including Feed Forward Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. 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.
- Learn about the inner workings of Deep Learning techniques
- Handle and pre-process data in Python using popular libraries such as Pandas, NumPy, Scikit-Learn
- Build Deep Learning models in Python using TensorFlow
- Apply what you have learned to real data sets
This course is part of the prestigious part-time Master in Finance program, conducted in English on Fridays and Saturdays on Campus Westend. It offers a valuable opportunity to network and gain expertise without committing to a full degree program. As the number of seats is limited, we recommend to register early. If you're a GBS or Goethe University alumni, explore our attractive alumni discount options.
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Participants have the choice of completing the course with a qualified certificate or attending as a guest auditor. As an auditor, you are not required to complete assignments, take exams or earn academic credits.
| ECTS | Certificate | |
|---|---|---|
| Guest auditor | - | Certificate of Participation |
| Full participant | 3 CP | Qualified Certificate |
This course can also be upgraded to a Certificate of Advanced Studies (CAS) in Financial Technology Management in combination with other courses from our Master in Finance program.
Assist. Prof. Dr. Kevin Bauer
Dr. 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 has been 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 in Behavioral Economics (Goethe University), Managerial Economics (University of Essex), and Applied Deep Learning in Finance (Goethe Business School).