Course: Data Mining and Machine Learning

Prof. Jacques Savoy & Ljiljiana Dolamic
University of Neuchatel
Computer Science Department

Main objective

The main objective of this course is to introduce the students to the various techniques and strategies that can be used to

• to discover pertinent relationships (or correlations) between variables
• to evaluate such relationships and machine learning approaches
• to know how to conduct a machine learning process based on available data and to interpret the results.

Practical exercises will complete the theoretical presentation.

Description

Introduction to Data Mining perspective; Association rules; Decision trees; Instance-based learning (nearest neighbors); clustering; Page Rank, HITS and spam.

The final mark is based on both a final written exam and the results of the practical exercices.

References

• Ian H. Witten, Eibe Frank: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufman, 2005.
• Jiawei Han, Micheline Kamber: Data Mining: Concepts and Techniques. Springer, 1999.
• Tom Mitchell: Machine Learning. McGraw Hill, 1997.
• Christopher M. Bishop: Pattern Recognition and Machine Learning. Springer, 2006.

Other references
• Richard J. Roiger, Michael W. Geatz: Data Mining (A Tutorial-Based Primer).Addison-Wesley Publishing, 2003.
• Soumen Chakrabarti: Mining the Web. Discovering Knowledge from Hypertext Data.Morgan Kaufmann, 2003.

Additional information

• The Machine Learning web site
• The ML data sets web site (around 160 different data sets)
• The random.org generation of values
• The CA (correspondance analysis) package

To obtain different tools

• The Weka software
• The official R web site
• The CRAN (Comprehensive R Archive Network) web site

Prof. Jacques Savoy
Université de Neuchâtel
Computer Science Department
Rue Emile-Argand 11
CH-2009 Neuchâtel
Switzerland


+41 32 718 1375 (phone)
+41 32 718 2701 (fax)