Computational Intelligence Methods
- Department of Applied Mathematics
Students will understand methods and techniques of computational intelligence that are mostly nature-inspired, parallel by nature, and applicable to many problems. They will learn how these methods work and how to apply them to problems related to data mining, control, intelligen games, optimizations, etc.
- Syllabus of lectures:
1. Introduction to computational intelligence, its uses.
2. Algorithms of machine learning.
3. Neural networks.
4. Evolutionary algorithms, evolution of neural networks.
5.  Computational intelligence methods: for clustering, for classification, for modeling and prediction.
6. Fuzzy logic.
7. Swarms (PSO, ACO).
8. Ensemble methods.
9. Inductive modeling.
10. Quantum and DNA computing.
11. Case studies, new trends.
- Syllabus of tutorials:
1. Introduction, getting acquainted with tools.
2. Introduction to the problems.
3. Course project assignment.
6. Project checkpoint.
9. Project checkpoint.
11. Report check.
12. Project presentations, workshop.
13. Project presentations, workshop.
14. Project presentations, workshop, assessment.
- Study Objective:
The module gives an overview of basic methods and techniques of computational intelligence that stem from the classical artificial intelligence. Computational intelligence methods are mostly nature-inspired, parallel by nature, and applicable to many problems in knowledge engineering.
- Study materials:
1. Konar, A. ''Computational Intelligence: Principles, Techniques and Applications''. Springer, 2005. ISBN 3540208984.
2. Bishop, C. M. ''Neural Networks for Pattern Recognition''. Oxford University Press, 1996. ISBN 0198538642.
- Further information:
- No time-table has been prepared for this course
- The course is a part of the following study plans: