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Instructors: Prof. Dr. rer. pol. Peter Buxmann; Dominik Jung
Event type:
Lecture
Org-unit: Dept. 01 - Law and Economics
Displayed in timetable as:
vl_KI
Subject:
Crediting for:
Hours per week:
1
Language of instruction:
German
Min. | Max. participants:
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Course Contents:
Modeling II, with focus on advanced modeling concepts, i.a.:
- Time Series Analysis
- Anomaly Detection
- Ensembles
- Neural Networks & Deep Learning
- Parameter Tuning
Evaluation
Deployment
Literature:
- Berthold, M. R.; Borgelt, C.; Ho¨ppner, F.; & Klawonn, F. (2010): Guide to intelligent data analysis: how to intelligently make sense of real data. Springer Science & Business Media.
- Cios, K. J.; Pedrycz, W.; Swiniarski, R. W.; & Kurgan, L. A. (2007): Data mining: a knowledge discovery approach. Springer Science & Business Media.
- Wirth, R., & Hipp, J. (2000): CRISP-DM: Towards a standard process model for data mining. In Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining (pp. 29-39). Citeseer.
- Witten, I.H.; Frank, E.; Hall, M.A. (2011): Data Mining: Practical Machine Learning Tools and Techniques, 3rd Edition, Morgan Kaufmann.
- Tan, P.; Steinbach, M.; Kumar, V. (2013): Introduction to Data Mining, Pearson Addison-Wesley.
- Han, J.; Kamber, M.; Pei, J. (2012): Data Mining – Concepts and Techniques, 3rd Edition, Morgan Kaufmann.
- Buxmann, P. & Schmidt, H. (2018): Künstliche Intelligenz: Mit Algorithmen zum wirtschaftlichen Erfolg, Springer-Verlag.
- Turban, E.; Aronson, J.E.; Liang, T.-P.; Sharda, R. (2007): Decision Support and Business Intelligence Systems, Pearson Prentice Hall.
Further literature will be announced in the lecture.
Preconditions:
Good programming skills, (Basic knowledge in functional and object-oriented programming concepts), Basic knowledge in statistics
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