Course: Data Mining and Machine Learning
Prof. Jacques Savoy
University of Neuchatel
Computer Science Department
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.
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.
- Ian H. Witten, Eibe Frank, Mark A. Hall: Data Mining: Practical Machine
Learning Tools and Techniques with Java Implementations. 3rd Ed., Morgan Kaufman, 2011.
- Trevor Hastie, Robert Tibshirani, Jerome Friedman:
The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag, New York (NY), 2nd Ed., 2009.
- Tom Mitchell: Machine Learning. McGraw Hill, 1997.
- Christopher M. Bishop: Pattern Recognition and Machine Learning. Springer, 2006.
To obtain different tools
- The Weka software
- The official R web site
- The CRAN (Comprehensive R Archive Network) web site