Knowledge acquisition by computational intelligence

Indicative content

1. Introduction into data mining (principles, explaining the basics and relevance of data mining in knowledge discovery form data).
2. Introduction into computational intelligence and machine learning for the purpose of interpreting knowledge form data.
3. Data types, their properties and conversion.
4. Distance and similarity metrics, normalization methods in data preparation in extracting knowledge from data.
5. The key areas in data mining: clustering, associative rules, outlier detection and classification.
6. Exploring the MATLAB and WEKA environment, working with the language Python.
7. Python in tasks from data preparation to visualization of results.
8. Types of neural networks and classification by neural networks.
9. Modelling rule-based systems and inference in discovering knowledge from data mining.
10. Evolutionary algorithms and their applicability.
11. Modeling recommender systems based on customers requirements, similarities among items and historical data
12. Interactive acquisition of knowledge from data (environments for unsupervised data mining)
13. Automatic acquisition of knowledge from data (environments for supervised data mining)