Lehrinhalte
Artificial Intelligence: Basics in Algorithms and Application:
[list]
[*]Introduction to AI & CRISP-DM
[*]Business & Data Understanding
[*]Data Preparation
[*]Modeling I, with focus on basic modeling concepts, i.a.:
[*]Clustering
[*]Classification
[*]Regression
[*]Association Analysis
[/list]
Literature
[list]
[*]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.
[/list]
Further literature will be announced in the lecture.
Voraussetzungen
Good programming skills, (Basic knowledge in functional and object-oriented programming concepts), Basic knowledge in statistics
Artificial Intelligence: Basics in Algorithms and Application:
[list]
[*]Introduction to AI & CRISP-DM
[*]Business & Data Understanding
[*]Data Preparation
[*]Modeling I, with focus on basic modeling concepts, i.a.:
[*]Clustering
[*]Classification
[*]Regression
[*]Association Analysis
[/list]
Literature
[list]
[*]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.
[/list]
Further literature will be announced in the lecture.
Voraussetzungen
Good programming skills, (Basic knowledge in functional and object-oriented programming concepts), Basic knowledge in statistics
- Lehrende: BuxmannPeter
Semester: WT 2021/22