There are two main things which make returning for Open Programs such a valuable and worthwhile experience. The first is being back on campus – meeting the current students, returning to campus again, the whole experience was just so pleasant. The second is the level of quality in the courses – the faculty teaching are absolute experts.
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.
This course is offered in the part-time Master in Finance 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.
Upon completion of this course, you will be able to:
- have learned 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 they have learned to real data sets
- Deep Learning
- Feed Forward Networks, Convolutional Networks, Recurrent Networks
- Data analysis in Python
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.
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.
€ 950 (fee is exempt from VAT). The fee for GBS students or alumni amounts to € 400.
|Fri., May 12, 2023||18:00 - 20:00|
|Sat., May 27, 2023||09:00 - 11:00, 11:30 - 13:30|
|Sat., Jun 10, 2023||14:30 - 16:30|
|Sat., Jun 24, 2023||09:00 - 11:00|
|Sat., Jul 8, 2023||09:00 - 11:00|
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