Event
Exercises to Knowledge Discovery [WS192511303]
Organisation
- Web Science
Part of
- Brick Knowledge Discovery | Industrial Engineering and Management (M.Sc.)
- Brick Knowledge Discovery | Economics Engineering (M.Sc.)
- Brick Knowledge Discovery | Information Systems (M.Sc.)
- Brick Knowledge Discovery | Information Engineering and Management (M.Sc.)
- Brick Knowledge Discovery | Economathematics (M.Sc.)
Literature
- T. Hastie, R. Tibshirani, J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (http://www-stat.stanford.edu/~tibs/ElemStatLearn/)
- T. Mitchell. Machine Learning. 1997
- M. Berhold, D. Hand (eds). Intelligent Data Analysis - An Introduction. 2003
- P. Tan, M. Steinbach, V. Kumar: Introduction to Data Mining, 2005, Addison Wesley
Appointments
- 14.10.2019 11:30 - 13:00 - Room: 05.20 1C-03
- 21.10.2019 11:30 - 13:00 - Room: 05.20 1C-03
- 28.10.2019 11:30 - 13:00 - Room: 05.20 1C-03
- 04.11.2019 11:30 - 13:00 - Room: 05.20 1C-03
- 11.11.2019 11:30 - 13:00 - Room: 05.20 1C-03
- 18.11.2019 11:30 - 13:00 - Room: 05.20 1C-03
- 25.11.2019 11:30 - 13:00 - Room: 05.20 1C-03
- 02.12.2019 11:30 - 13:00 - Room: 05.20 1C-03
- 09.12.2019 11:30 - 13:00 - Room: 05.20 1C-03
- 16.12.2019 11:30 - 13:00 - Room: 05.20 1C-03
- 23.12.2019 11:30 - 13:00 - Room: 05.20 1C-03
- 13.01.2020 11:30 - 13:00 - Room: 05.20 1C-03
- 20.01.2020 11:30 - 13:00 - Room: 05.20 1C-03
- 27.01.2020 11:30 - 13:00 - Room: 05.20 1C-03
- 03.02.2020 11:30 - 13:00 - Room: 05.20 1C-03
Note
The exercises are based on the lecture Knowledge Discovery. Several exercises are covered, which take up and discuss in detail the topics covered in the lecture Knowledge Discovery. Practical examples are demonstrated to the students to enable a knowledge transfer of the theoretical aspects learned into practical application.
Contents of the lecture cover the entire machine learning and data mining process with topics on monitored and unsupervised learning processes and empirical evaluation. The learning methods covered range from classical approaches like decision trees, support vector machines and neural networks to selected approaches from current research. Learning problems considered include feature vector-based learning and text mining.
Learning objectives:
Students
- know fundamentals of Machine Learning, Data Mining and Knowledge Discovery.
- are able to design, train and evaluate adaptive systems.
- conduct Knowledge Discovery projects in regards to algorithms, representations and applications.