Special Course in Machine Learning: Geometric Deep Learning
Geometric Deep Learning (GDL) is an effort to exploit the geometric structure of datasets to achieve efficient representation learning. But GDL also proposes a fundamental principle (symmetry) as a framework to understand and classify the current (and future) zoo of diverse neural networks.
In this course, we are going to delve into geometric deep learning theory based on the public online course by Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Velickovic: https://geometricdeeplearning.com/lectures/
Syllabus
Lecture 1: Introduction
Lecture 2: High-Dimensional Learning
Lecture 3: Geometric Priors I
Lecture 4: Geometric Priors II
Lecture 5: Graphs & Sets I
Lecture 6: Graphs & Sets II
Lecture 7: Grids
Lecture 8: Groups
Lecture 9: Geodesics & Manifolds
Lecture 10: Sequences & Time Warping
Lecture 11: Conclusions
Tutorial 1: Graph Neural Networks
Seminar 1: Geometric Deep Learning and Reinforcement Learning
Prerequisites
This course requires a strong background in math and deep learning.
- A second course in Algebra
- Calculus
- Geometry
- Deep learning
- Programming
Organization
The course consists of video lectures and tests/project. Each week we will meet to watch one lecture from https://geometricdeeplearning.com/lectures/ in which we will stop the video frequently to discuss. It will be possible to participate in the discussion/tests fully online via Zoom for those students who cannot attend physically.
The first lecture will take place on Friday, September 10th.
The class is held in Delta room 2049 on Fridays at 12:15-14. For each class one of the teachers/students (or team) will produce test questions for the video lecture of that week. NB! The test must be sent to raulvicente@gmail.com by Friday before the class for review!
The last 4 weeks are reserved for a team project. The idea of a project is to implement some concepts covered during the course.
To pass the course one has to:
- create one test and score more than 70% in all other tests,
- participate in a team project.
Materials
- https://geometricdeeplearning.com/lectures/ - we will mostly follow this course.
- https://arxiv.org/abs/2104.13478 - arXiv paper with most of the content of the lectures.
Contacts
Raul Vicente, raulvicente@gmail.com, room 3100
Florian Stelzer, fl.stelzer@gmail.com
Kallol Roy, kallol.roy@ut.ee