This module introduces Deep Learning methods and their applications in modern predictive analytics and business decision-making. It examines how Machine Learning techniques inspired by the human brain are used to create competitive advantages across industries. The course covers core Deep Learning concepts, including Feed Forward Neural Networks, as well as applications in natural language processing and predictive tasks such as credit default, customer churn, and stock price prediction. Students also explore interpretability methods in response to growing concerns and regulations surrounding the “black box” nature of AI systems.
- Understand the inner workings of Deep Learning techniques
- Handle and pre-process data in Python using popular libraries such as Pandas, NumPy, Scikit-Learn, and Tensorflow
- 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
This course is part of the prestigious part-time Goethe MBA Digital Transformation | Data Science | Sustainability, 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. Upon completion, participants receive a Certificate of Participation. This course may be credited towards the Certificate of Advanced Studies (CAS) in Data Science & AI when completed together with the other required courses within one academic year. As the number of seats is limited, we recommend to register early. If you're a GBS or Goethe University alum, explore our attractive alumni discount options.
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Prof. Dr. Kevin Bauer
Dr. Kevin Bauer is Professor of Applied AI at Goethe University Frankfurt, integrated into Hessian.AI, and a bridge professor at the Leibniz Institute for Financial Research SAFE. His research focuses on explainable AI, causal machine learning, and human information processing at the intersection of technology and economics. His work has been published in leading journals, including Information Systems Research and Management Science. He has received several research awards, including the JAIS Best Paper Award, ISR Best Paper Award, and AIS Senior Scholar Award, and serves on the Editorial Review Board of Information Systems Research.
Candidates with an undergraduate degree (i.e., bachelor or equivalent) of 180 credit points (CP) based on the European Credit Transfer System (ECTS) can apply for our Open Programs. The allocation of credit points for work experience will appear on the Academic Transcript of Records and be titled ‘Prior Learning Assessment’.
Open Program applicants must submit a recent proof of English language proficiency at the higher B2 level per the Common European Framework of Reference (CEFR). The GBS Office of Admissions will accept test results of the following tests, taken no longer than two years ago upon the date of submission:
- TOEFL iBT®
- TOEFL iBT® Home Edition
- IELTS
- Cambridge Certificate of Proficiency in English
- English Language Level Test (ELLT) - Oxford International Digital Institute
Applicants may request a waiver of proof of proficiency in English in case one of the following conditions applies. The applicant
- is a native speaker of English;
- has graduated from an international baccalaureate program
- has completed their undergraduate or master-level studies in a program taught entirely in English;
- has worked in an English-speaking country for at least one year.
If the applicant has not taken any of the above tests when submitting the application, the applicant can upload a confirmation of the test registration to the online admissions portal.
Applicants who submit a language certificate older than two years but without an expiry date will be invited for an additional interview with a native speaker of English.
Proof of relevant post-undergraduate work experience of at least one year is to be submitted, e.g., a performance review, reference letter, or written confirmation from your employer.