Course: Data Mining and Machine Learning
Prof. Jacques Savoy & Olena Zubaryeva
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