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-GNN Graph Neural Networks Extent of teaching: 1P+1C Instructor: Čepek M. Completion: Z,ZK Department: 18105 Credits: 4 Semester: L Annotation:
The course introduces students to advanced artificial intelligence techniques for working with graphs. Lectures will focus on the latest graph neural networks for creating vector representations of nodes, edges and entire graphs. The techniques discussed cover various types of graphs, including time-varying graphs. The last part of the course also covers graph generation and interpretability of graph neural networks. In the exercises, students will try out selected techniques and problems.
Lecture syllabus:
1) Introduction to the subject, motivation and definition of terms. 2) Representations based on the adjacency matrix and random walks through the graph. 3) Convolutional graph neural networks. 4) Representations of time-variable graphs. 5) Graph generation and representation using graph autoencoders. 6) Interpretability and applications in natural language processing and recommender systems. Seminar syllabus:
1) Introduction to the StellarGraph library. 2) Vector representation of graphs. 3) Classification and clustering of nodes and graphs. 4) Graphs with time component. 5) Working on a semestral project. 6) Submission of the project and its presentation. Literature:
Deep Learning; I. Goodfellow, Y. Bengio, A. Courville; MIT Press; 2016; ISBN 978-0262035613. Introduction to Graph Neural Networks; Zhiyuan Liu, Jie Zhou; Morgan & Claypool Publishers; 2020; ISBN-13? 978-1681737652 Graph Representation Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning); William L. Hamilton; Morgan & Claypool Publishers; 2020; ISBN? 978-1681739632 Heterogeneous Graph Representation Learning and Applications; Chuan Shi, Xiao Wang, Philip S. Yu; Springer; 2022; ISBN:? 978-9811661655Requirements:
no entry requirements
Informace o předmětu a výukové materiály naleznete na https://courses.fit.cvut.cz/NI-GNN/ The course is also part of the following Study plans:
Study Plan Study Branch/Specialization Role Recommended semester NI-TI.2018 Computer Science V Není NI-PB.2020 Computer Security V Není NI-ZI.2020 Knowledge Engineering V Není NI-SPOL.2020 Unspecified Branch/Specialisation of Study V Není NI-TI.2020 Computer Science V Není NI-TI.2023 Computer Science V Není NI-NPVS.2020 Design and Programming of Embedded Systems V Není NI-PSS.2020 Computer Systems and Networks V Není NI-MI.2020 Managerial Informatics V Není NI-SI.2020 Software Engineering (in Czech) V Není NI-SP.2020 System Programming V Není NI-WI.2020 Web Engineering V Není NI-SP.2023 System Programming V Není
Page updated 24. 4. 2024, semester: Z/2020-1, Z/2019-20, Z/2023-4, Z/2021-2, L/2022-3, Z/2024-5, L/2019-20, Z/2022-3, L/2020-1, L/2021-2, L/2023-4, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška