Lab Course: Graph Learning
Summer term 2019
Contact
Name
Content
In this practical course the participants will implement learning algorithms for graph data. This involves in particular topics such as graph kernels and graph neural networks which they will apply to different datasets.
Prerequisits
This pro seminar is only addressed to master students in the MSc Computer Science and MSc Data Science programmes.
No previous knowlege is required. Knowledge from the courses Algorithmic Foundations of Datascience, Algorithmic Learning Theory, Machine Learning are helpful, as well as knowledge of Python3 and Tensorflow.
Organization
The tasks will be worked upon by groups of two or three students.
Time and Place
Precise dates will be announced in RWTHonline before the semster starts.
The course will have a weekly meeting where results will be presented and possibly basics of graph learning will be presented.
Instructors
Assistants:
Hinrikus Wolf and Martin Ritzert
Requirements
Each participant of the seminar will be assigned a specific chapter from one of the books "Information and Coding Theory" by Jones & Jones and "A Concise Introduction to Data Compression" by Salomon. They are expected to give a talk of about 30 minutes about it and write a paper of about 5 pages summarising it.
The topics will be assigned at the first meeting of the seminar.