INFO-H-423: Data Mining

Version 2018 Data Mining Edition 2018

• Mahmoud Sakr
• mahmoud.sakr@ulb.ac.be
• Room entrance S. UB4.131, then third room to the right.

• Theory 24 h - Exercises 24h - Project 12h
• 5 ECTS

• Master in Computer Science and Engineering [MA-IRIF]
• Master in Computer Sciences [INFO]
• Erasmus Mundus Joint Master Degree Programme in Big Data Management and Analytics (BDMA)

The grade for the course will be composed of:

• 25% group project.
• 75% written exam.

Data Mining aims at finding useful regularities in large structured and unstructured data sets. The goal of this course is to get a fundamental understanding of popular data mining techniques strengths and limitations, as well as their associated computational complexity issues. It will also identify industry branches which most benefit from Data Mining such as health care and e-commerce. The course will focus on business solutions and results by presenting case studies using real (public domain) data. The students will use recent Data Mining software.

1. Of the following three books, several chapters serve as background reading supporting the lectures.
   • Data Mining The Textbook (Springer; 2015) by Aggarwal, Charu C.
     Available for download through Cible+@ULB

1. Lecture slides: will be made available on this site before the lectures.

• Tools used during the course
  • Rapidminer