1.Introduction to knowledge discovery and data mining, data characteristics

2.Data preparation a. preprocessing b. transformation

3. Classification a. Decision trees b. Bayessian (Naïve Bayes) c. distance-based d. regression e. neural networks f. support vector machines

4. Clustering a. partitioning algorithms b. hierarchical clustering c. probabilistic clustering d. self-organizing maps and neurla networks

5. Association rules

6. Text and web mining

7. Evaluation of data mining methods

Type of methodology: Combination of lecture and hands-on

Participants receive the certificate of attendance: Yes

Paid training activity for participants: Yes, for all

Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge