Data Analysis and Machine Learning


The main Data Mining techniques will be addressed during this course. More precisely, association rules, classification (or, supervised learning) and clustering (or, unsupervised learning). The main objective for the students is (i) to be able to identify which kind of knowledge (association vs classification model vs segmentation) is needed depending on the practical problem they are faced and (ii), the pros and limitations of each technique. Practical exercises will be performed using free software like Weka or Rapidminer.


Teaching: 20h Lecture + 27h practical work
Examination: practical work + final exam
Lecturer: Sofian Maabout

Recommended Readings

Data Mining: Concepts and Techniques (3rd ed) Jiawei Han, Micheline Kamber & Jian Pei Published by Morgan Kaufmann ISBN 9780123814791