Course contents: Introduction to data mining techniques (data, problems, applications). General data analysis and management techniques. Data categorization (decision trees, statistical methods). Categorization of multidimensional data. Clustering techniques. Techniques for relation and knowledge extraction. Applications of data mining techniques to searching.

Assessment: Written exercises and implementation of a project during the semester and written exams at the end of the semester. The final grade results by taking into account the respective marks of exercises (10-20%), project (30-40%) and final examination (50-80%). It is required to have a passing grade on the project and the final examination.