Graph Deep Learning
People
Course director
Description
Graphs are structures commonly found across science to describe the complex relations that exist among the entities of a system, like chemical bonds between atoms, friendships and personal relations on social media, the spread of diseases among countries. Recent advances in deep learning have focused on developing neural networks able to process graph-based structures by leveraging on node and relational information to solve a machine learning task. Students will learn how to process graphs by embedding them to vector spaces for traditional and deep processing as well as design and implement graph convolutional networks. The course will also cover some basic theoretical aspects to understand the underlying mechanisms.
Objectives
The goal of this course is to provide the fundamentals of graph deep learning by presenting the core components and related applications.
Teaching mode
In presence
Learning methods
In addition to frontal lectures, practical laboratory sessions in Python will provide the due hands on.
Examination information
Assignment and exam
Education
- Master of Science in Artificial Intelligence, Lecture, 2nd year
- Master of Science in Computational Science, Lecture, Elective, 1st year
- Master of Science in Computational Science, Lecture, Elective, 2nd year
- Master of Science in Informatics, Lecture, Artificial Intelligence, Elective, 2nd year
- Master of Science in Informatics, Lecture, Geometric and Visual Computing, Elective, 1st year
- Master of Science in Informatics, Lecture, Geometric and Visual Computing, Elective, 2nd year
- PhD programme of the Faculty of Informatics, Lecture, Elective, 1st year (2.0 ECTS)
Prerequisite
- Machine Learning, Wand M., Ashley D. R., Faccio F., Gopalakrishnan A., Herrmann V., Kirsch L., SA 2021-2022
