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Master in Information and Computer Sciences

Knowledge Discovery and Data Mining
Module:Module 2.8, Semester 2
Objective: We understand Data Mining (Knowledge Discovery) as a life-cylce process from  data to information and insights. In times of Big data, Data Mining has become a central interest both for industry and academia. In this course, we discuss several data-related aspects like preprocessing or pricacy as well as selected aspects of Machine Learning. An expansive definition of Data Mining, which is the derivation of insights from masses of data by studying and understanding the structure of the constituent data, and selected applications complete the course.

Course learning outcomes: * Explain the fundamental concepts of data mining and knowledge discovery
* List the properties of data relevant for deriving interesting and useful information/observation from that.
* Explain machine learning algorithms and strategies to deploy the discovered results
* Argue the importance of domain knowledge during the data analysis with its scope and limitations

Description: * Definition and Process.
* Data Mining, Data Science, and the Big Data Hype.
* Data Quality and Preprocessing
* Data Privacy and Security.
* Data and Information Visualization.
* Machine Learning for Clustering, Classification, Association Discovery, Sequential Pattern Analysis, and/or Time Series Analysis.


The course is organised as a lecture with integrated exercises. It follows the "Information Retrieval" course and will itself be continued in Semester 3 by a more intensive discussion about "Machine Learning". Each participant must be inscribed via Moodle. Course material will be uploaded regularly.


Language: English
Lecturer: SCHOMMER Christoph
Rating: 60% oral or written examination; 40% midterm tests

Selected references:

* M. Berry, G. Linoff: Mastering Data Mining, John Wiley & Sons, 2000.
* U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, R. Uthurusamy: Advances in Knowledge
Discovery and Data Mining, AAAI/MIT Press, 1996.
* J. Han, M. Kamber: Data Mining: Concepts and Techniques, 2nd edition, Morgan
Kaufmann, ISBN 1558609016, 2006.
* I. Witten, E. Frank, M. Hall: Data Mining: Practical Machine Learning Tools and
Techniques, 3nd Edition, Morgan Kaufmann, 2011.