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.
NI-MVI Computational Intelligence Methods Extent of teaching: 2P+1C Instructor: Kordík P. Completion: Z,ZK Department: 18105 Credits: 5 Semester: Z Annotation:
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.
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. 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. 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:
Page updated 26. 4. 2024, semester: Z/2020-1, L/2021-2, L/2019-20, L/2022-3, Z/2019-20, L/2020-1, L/2023-4, Z/2022-3, Z/2021-2, Z/2023-4, Z/2024-5, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška