Computational Intelligence Methods

Garant předmětu:

Pavel Kordík

Lecturer:

Pavel Kordík

Tutor:

Pavel Kordík, Alexander Kovalenko

Supervisor:

Department of Applied Mathematics

Synopsis:

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.

Requirements:

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. Stallings, W. : Data and Computer Communications (10th Edition). Prentice Hall, 2013. ISBN 0133506487.

2. Aracil, J. - Callegati, F. (Eds.) : Enabling Optical Internet with Advanced Network Technologies. Springer, 2009. ISBN 978-1-84882-278-8.

3. Van Beijnum, I. : BGP. Building Reliable Networks with the Border Gateway Protocol. O'Reilly Media, 2002. ISBN 978-0-596-00254-1.

4. W. A. Flangan : VoIP and Unified Communications: Internet Telephony and the Future Voice Network. Wiley, 2012. ISBN 1118019210.

Note:

Further information:

https://courses.fit.cvut.cz/NI-MVI/

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:

• Master specialization Computer Science, in Czech, 2018-2019 (PS, elective course)
• Master specialization Computer Security, in Czech, 2020 (elective course)
• Master specialization Design and Programming of Embedded Systems, in Czech, 2020 (elective course)
• Master specialization Computer Systems and Networks, in Czech, 202 (elective course)
• Master specialization Management Informatics, in Czech, 2020 (elective course)
• Master specialization Software Engineering, in Czech, 2020 (elective course)
• Master specialization System Programming, in Czech, version from 2020 (elective course)
• Master specialization Web Engineering, in Czech, 2020 (elective course)
• Master specialization Knowledge Engineering, in Czech, 2020 (PS, elective course)
• Master specialization Computer Science, in Czech, 2020 (PS, elective course)
• Mgr. programme, for the phase of study without specialisation, ver. for 2020 and higher (VO, elective course)
• Master specialization Computer Science, in English, 2021 (VO)
• Master Specialization Digital Business Engineering, 2023 (VO)
• Master specialization System Programming, in Czech, version from 2023 (elective course)
• Master specialization Computer Science, in Czech, 2023 (elective course)