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Instructors: Dr. rer. pol. Timo Sturm
Event type:
Exercise
Org-unit: Dept. 01 - Law and Economics
Displayed in timetable as:
ue_KI
Subject:
Crediting for:
Hours per week:
1
Language of instruction:
German
Min. | Max. participants:
- | -
Course Contents:
Both parts of the module include a lecture to convey the theoretical concepts as well as accompanying exercises in which the concepts can be applied on the basis of practical questions. In addition to the lectures, the participants work independently in project groups in cooperation with companies on an AI project to answer an analytical question and implement a corresponding AI solution. This enables the participants to transfer the theoretical contents to a concrete, practical application context.
Artificial Intelligence: Advanced Topics in Algorithms and Application:
- 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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