Course description

Main page | Study Branches/Specializations | Groups of Courses | All Courses | Roles Instructions

A course is the basic teaching unit, it's design as a medium for a student to acquire comprehensive knowledge and skills indispensable in the given field. A course guarantor is responsible for the factual content of the course.
For each course, there is a department responsible for the course organisation. A person responsible for timetabling for a given department sets a time schedule of teaching and for each class, s/he assigns an instructor and/or an examiner.
Expected time consumption of the course is expressed by a course attribute extent of teaching. For example, extent = 2 +2 indicates two teaching hours of lectures and two teaching hours of seminar (lab) per week.
At the end of each semester, the course instructor has to evaluate the extent to which a student has acquired the expected knowledge and skills. The type of this evaluation is indicated by the attribute completion. So, a course can be completed by just an assessment ('pouze zápočet'), by a graded assessment ('klasifikovaný zápočet'), or by just an examination ('pouze zkouška') or by an assessment and examination ('zápočet a zkouška') .
The difficulty of a given course is evaluated by the amount of ECTS credits.
The course is in session (cf. teaching is going on) during a semester. Each course is offered either in the winter ('zimní') or summer ('letní') semester of an academic year. Exceptionally, a course might be offered in both semesters.
The subject matter of a course is described in various texts.

NIE-MVI Computational Intelligence Methods Extent of teaching: 2P+1C

Instructor: Čepek M., Kordík P. Completion: Z,ZK

Department: 18105 Credits: 5 Semester: Z

Annotation:
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.

Lecture syllabus:

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 syllabus:

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.

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.

Requirements:
BI-ZUM - Introduction to artificial intelligence.

Informace o předmětu a výukové materiály naleznete na https://courses.fit.cvut.cz/NI-MVI/

The course is also part of the following Study plans:

Study Plan Study Branch/Specialization Role Recommended semester

NIE-SI.21 Software Engineering 2021 V Není

NIE-TI.21 Computer Science 2021 V Není

NIE-DBE.2023 Digital Business Engineering V Není

NIE-NPVS.21 Design and Programming of Embedded Systems 2021 V Není

NIE-PSS.21 Computer Systems and Networks 2021 V Není

NIE-PB.21 Computer Security 2021 V Není

NIE-SI.21 Software Engineering 2021 V 3

NIE-NPVS.21 Design and Programming of Embedded Systems 2021 V 3

NIE-PSS.21 Computer Systems and Networks 2021 V 3

NIE-PB.21 Computer Security 2021 V 3

NIE-TI.21 Computer Science 2021 PS 3

Page updated 20. 4. 2024, semester: L/2023-4, L/2020-1, L/2022-3, L/2021-2, Z/2019-20, Z/2022-3, Z/2020-1, Z/2023-4, L/2019-20, Z/2021-2, Z/2024-5, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška

NIE-MVI	Computational Intelligence Methods			Extent of teaching:	2P+1C
Instructor:	Čepek M., Kordík P.			Completion:	Z,ZK
Department:	18105	Credits:	5	Semester:	Z

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.

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.

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.