Learning Objectives: The module is concerned with recent developments and emerging technologies in the field of Information Systems. Students have the opportunity to develop the following skills: Students further develop their knowledge and understanding of the theories, applications and methods of Information Systems.Students are able to critically appraise recent IS trends and developments using established IS theories and practices. Students further develop their ability to conduct scholarly research, concentrating on academic writing, information retrieval and literature analysis.
Information from Moodle:
Welcome to our master seminar, in which you will grow your excellency in applied machine learning and develop your skills as a researcher. The seminar is designed as a follow-up to our graduate lectures and will ready you for writing a master dissertation in the wide scope of data science.
The overarching topic of this year’s seminar is Applied Machine Learning for Smart Home and Digital Marketing. Within this scope, we have compiled a set of seminar topics that provide you with an opportunity to learn about recent developments in artificial intelligence research and real-world applications.
The seminar targets master students in their third study semester. Ideally, you have completed our MSc. modules Business Analytics & Data Science (BADS) and Advanced Data Analytics for Management Support (ADAMS) prior to taking the seminar. Specifically, we expect a solid understanding of (deep) machine learning and data science as well as proficiency in Python and/or R programming from every participant. These competencies can be acquired in the above modules but also elsewhere. Therefore, completion of BADS and ADAMS is recommended but not a mandatory requirement to participate in the seminar. Completion of other modules in the scope of computational statistics, econometrics, and machine learning prior to attending the seminar is useful but is not a requirement.
Part of the seminar: Ungraded presentation of the term paper and discussion.
Audience: master students in the 3rd semester (not suitable for students in the 1st semester)
Participation limit: 24
Registration for the seminar takes place online via AGNES till October 11, 2023. |
