Event

• Baier, Lucas, Niklas Kühl, and Gerhard Satzger. "How to Cope with Change?-Preserving Validity of Predictive Services over Time." Proceedings of the 52nd Hawaii International Conference on System Sciences. 2019.
• Cawley, Gavin C., and Nicola LC Talbot. "On over-fitting in model selection and subsequent selection bias in performance evaluation." Journal of Machine Learning Research 11.Jul (2010): 2079-2107.
• Fromm, Hansjörg, Francois Habryn, and Gerhard Satzger, “Service analytics: Leveraging data across enterprise boundaries for competitive advantage,” in Globalization of Professional Services, 2012, pp. 139–149.
• Gama, J, I. Žliobaitė, A. Bifet, M. Pechenizkiy, and A. Bouchachia, “A survey on concept drift adaptation,” ACM Comput. Surv., vol. 46, no. 4, pp. 1–37, 2014.
• Hirt, Robin, Niklas Kühl, and Gerhard Satzger. "An end-to-end process model for supervised machine learning classification: from problem to deployment in information systems." Designing the Digital Transformation: DESRIST 2017 Research in Progress Proceedings of the 12th International Conference on Design Science Research in Information Systems and Technology. Karlsruhe, Germany. 30 May-1 Jun. Karlsruher Institut für Technologie (KIT), 2017.
• Hirt, Robin, and Niklas Kühl. "Cognition in the Era of Smart Service Systems: Inter-organizational Analytics through Meta and Transfer Learning." (2018).
• Hirt, Robin, Niklas Kühl, and Gerhard Satzger. "Cognitive computing for customer profiling: meta classification for gender prediction." Electronic Markets 29.1 (2019): 93-106.
• Kühl, N., Goutier, M., Hirt, R., & Satzger, G. (2019, January). Machine learning in artificial intelligence: Towards a common understanding. In Proceedings of the 52nd Hawaii International Conference on System Sciences.
• Kühl, Niklas, Marius Mühlthaler, and Marc Goutier. "Supporting customer-oriented marketing with artificial intelligence: automatically quantifying customer needs from social media." Electronic Markets (2019): 1-17
• Martin, Dominik, Robin Hirt, and Niklas Kühl. "Service Systems, Smart Service Systems and Cyber-Physical Systems—What’s the difference? Towards a Unified Terminology." (2019).
• Müller, Vincent C., and Nick Bostrom. "Future progress in artificial intelligence: A survey of expert opinion." Fundamental issues of artificial intelligence. Springer, Cham, 2016. 555-572.
• Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359.

Artificial Intelligence (AI) and the application of machine learning is becoming more and more popular to solve relevant business challenges. However, it is not only important to be familiar with precise algorithms, but rather a general understanding of the necessary steps w ith a holistic view—from real-world challenge to successful deployment of an AI-based solution. As part of this course, we teach the complete lifecycle of an AI project with a focus on supervised machine learning challenges. We do so by also teaching the use of Python and the required packages like scikit-learn and tensorflow with exemplary data. We then take this knowledge to the more complex case of service systems with different entities (e.g., companies) who interact with each other and show possibilities on how to derive holistic insights. Two possibilities to do so are the use of meta and transfer machine learning, where we teach insights in their theory, design and application.

Students of this course will be able to understand and implement the complete lifecycle of a typical Artificial Intelligence use case with supervised machine learning. Furthermore, they understand the importance and the means of applying AI and Machine Learning within service systems, which allows multiple, independent entities to collaborate and derive insights. Students will be proficient with typical Python code for AI challenges.