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

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 such as deep learning, LASSO, and support vector machines. You will get familiar with text mining, and the tools you need to analyze the various facets of data sets in practice. 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.
• De Prado, M. L. (2018). Advances in Financial Machine Learning. John Wiley & Sons.
• Goodfellow, I., Bengio, Y., and A. Courville (2017). Deep Learning. MIT Press.
• Hastie, T., Tibshirani, R., and J. Friedman (2009). Elements of Statistical Learning. Second Edition. Springer.
• James, G., Witten, D., Hastie, T., and R. Tibshirani (2013). An Introduction to Statistical Learning: with Applications in R. Springer.
• HWitten, I. H., Eibe, F., Hall, M. A., Pal, C. J. (2016). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.