Informationsdienste und elektronische Märkte
Prof. Dr. Andreas Geyer-Schulz | Schroff-Stiftungslehrstuhl

Institut für Wirtschaftsinformatik und Marketing
Fakultät für Wirtschaftswissenschaften

Informationsdienste und elektronische Märkte > Studium

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Inhalt

Important Information:

This course will be a combination of live tutorial sessions via Zoom and recorded videos for the lecture. All content and current information can be found in the Ilias course, accompanying literature here on our website.

Zoom Information:

Time: Every week on Mon., 02:00 PM to 03:30 PM

First Session: 02.11.2020

Join Zoom Meeting: https://kit-lecture.zoom.us/j/62511703042

Meeting ID: 625 1170 3042

Zoom Information for Tutorial:

Time: Bi-weekly on Mon., 03:45 PM to 05:15 PM

First Session: 09.11.2020

Join Zoom Meeting: https://kit-lecture.zoom.us/j/68240338362

Meeting ID: 682 4033 8362

Course Description:

In recent years, the volume, variety, velocity, veracity, and variability of available data have increased due to improvements in computational and storage power. The rise of the Internet has made available large sets of data that allow us to use and merge them for different purposes. Data science helps us to extract knowledge from the continually-increasing large datasets. This course will introduce students to a wide range of machine learning and statistical techniques. Students will learn theory and concepts with real data sets from different disciplines such as finance, marketing, and business.

Literature:

• Alpaydin, E. (2014). Introduction to Machine Learning. Third Edition, MIT Press.
• Hall, J. (2020). Machine Learning in Business: An Introduction to the World of Data Science. Independently published.
• James, G., Witten, D., Hastie, T., and R. Tibshirani (2013). An Introduction to Statistical Learning: with Applications in R. Springer.
• Tan, P. N., Steinbach, M., Karpatne, A., & Kumar, V. (2018). Introduction to data mining. Pearson.