Description:

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.

Contents:

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.

Seminar contents:

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.

Recommended literature:

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.

Keywords:

Machine learning, neural networks, evolutionary algorithms, neuroevolution, fuzzy logic, swarms, ensemble methods, quantum and DNA computing

Abbreviations used:

Semester:

• W ... winter semester (usually October - February)
• S ... spring semester (usually March - June)
• W,S ... both semesters

Mode of completion of the course:

• A ... Assessment (no grade is given to this course but credits are awarded. You will receive only P (Passed) of F (Failed) and number of credits)
• GA ... Graded Assessment (a grade is awarded for this course)
• EX ... Examination (a grade is awarded for this course)
• A, EX ... Examination (the award of Assessment is a precondition for taking the Examination in the given subject, a grade is awarded for this course)

Weekly load (hours per week):

• P ... lecture
• C ... seminar
• L ... laboratory
• R ... proseminar
• S ... seminar