“Machine Learning in Marketing: Theory and Applications” (WS 2020/21)
I have developed this course with master students from quantitative fields such as marketing, economics, statistics and computer science in mind. The course’s goal is to prepare students in their last year of study for solving real-world marketing problems using modern quantitative methods. The course first reviews theoretical foundations in marketing, statistics, and probability theory and then shows how to formalize marketing decisions as machine learning problems. It also equips students with the necessary tools to implement machine learning pipelines efficiently.
Guest Lectures “Machine Learning in Marketing”
My guest lectures (e.g., MIT Sloan School of Management, WHU School of Management) are based on my research as well as my industry experience that I have gained from founding several machine learning start-ups. I present real-world results derived from field experiments that evaluate the economic benefits of promotion personalization based on modern machine learning stacks.