Computational Intelligence Methods

Login to KOS for course enrollment Display time-table
Code Completion Credits Range Language
NIE-MVI Z,ZK 5 2P+1C English
Garant předmětu:
Pavel Kordík
Miroslav Čepek, Pavel Kordík
Miroslav Čepek, Pavel Kordík
Department of Applied Mathematics

Students will understand the basic methods and techniques of computational intelligence, which are based on traditional artificial intelligence, are parallel in nature and are applicable to solving a wide range of problems. The subject is also devoted to modern neural networks and the ways in which they learn and neuroevolution. Students will learn how these methods work and how to apply them to problems related to data extraction, management, intelligence in games and optimisation, etc.


BI-ZUM - Introduction to artificial intelligence.

Syllabus of lectures:

1. Introduction to computational intelligence methods, application demonstrations.

2. Machine learning and heuristics to solve ML problems.

3. Evolutionary algorithms, schema theory

4. Neural networks and gradient learning.

5. Convolutional neural networks.

6. Autoencoders and convnets.

7. Embeddings, graph representations, word2vec.

8. Recurrent neural networks, attention.

9. Transformers.

10. Variantional Autoencoders (VAE), Generative Networks (GANs).

11. Neuroevolutions, hypernets.

12. Meta-learning, few shot learning, AutoML.

Syllabus of tutorials:

1. Introduction, getting acquainted with tools.

2. Introduction to the problems.

3. Course project assignment.

4. Consultations.

5. Consultations.

6. Project checkpoint.

7. Consultations.

8. Consultations.

9. Project checkpoint.

10. Consultation.

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.

3. Goodfellow, I. - Bengio, Y. - Courville, A. : Deep Learning (Adaptive Computation and Machine Learning series). MIT Press, 2016. ISBN 978-0262035613.

Further information:
Time-table for winter semester 2024/2025:
Time-table is not available yet
Time-table for summer semester 2024/2025:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2024-04-11
Aktualizace výše uvedených informací naleznete na adrese https://bilakniha.cvut.cz/en/predmet6700206.html