This course provides an in-depth exploration of key data mining concepts and techniques, equipping students with the skills to extract valuable insights from data. Students will learn about data preprocessing, association rules, classification methods, clustering, and support vector machines through both theoretical study and hands-on implementation.

Temps présentiel : 37.5 heures

Charge de travail étudiant : 150 heures

Méthode(s) d'évaluation : Examen écrit

Référence :
"Introduction to Data Mining" by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar