Geometric Deep Learning
Ph.D. -course
- ECTS credits
- 3
- Teaching semesters
- Spring
- Course code
- NORAINF903
- Number of semesters
- 1
- Resources
- Schedule
Course description
Course content
Modern deep learning has had tremendous success in applying complex neural networks to problems from a
wide range of disciplines, such as computer vision and protein folding. Geometric deep learning deals with
incorporating symmetries into deep learning architectures. A symmetry of features is a transformation that is
guaranteed not to change the labels. Symmetries are ubiquitous in many machine learning tasks. For example,
in computer vision the object category is unchanged by shifts, so shifts are symmetries in the problem of visual
object classification. In computational chemistry, the task of predicting properties of molecules independently
of their orientation in space requires rotational invariance. This course gives and understanding of the theoretical
basis underlying geometric deep learning. Furthermore, the course includes implementation of geometric
components and as well as applying geometric deep learning on real-world data.
Learning outcomes
Content:
- Geometry
- Geometric priors
- Learning on graphs and sets
- Group-equivariant learning
- Learning on manifolds
Learning objectives:
Upon completion of the course the student be able to
- understand the basic principles of geometric deep learning
- implement geometric deep learning algorithms
- compare various approaches in geometric deep learning
- read and critically assess geometric deep learning papers
- apply and evaluate geometric deep learning methods on real data sets
Study period
Credits (ECTS)
Course location
Language of instruction
Course registration and deadlines
Who may participate
Same as NORA research school: