This course was designed to get you up to speed with Graph Neural Networks so that you can both understand seminal papers in the field and implement GNNs using modern software tools. If you're wanting to understand models like Graph Convolutional Networks and Graph Attention Networks (i.e., "deep learning on graphs"), this course will help you get there. It was constructed around three elements:

• Theory, to familiarize you with the foundational topics quickly and efficiently (i.e., not more complicated than it needs to be)
• Practice, to walk you thru Hands-On examples that span the major applications of Node Classification, Link Prediction and Graph Classification and discuss practical engineering problems. This section runs with cohorts of students and therefore enrollment is limited.
• Community, to get you engaged with fellow students in the form of group discussion and code reviews. Since this works best with a cohort of students, it will be wrapped into the "Hands-On" section.

The Theory section consists of self-paced videos, quizzes and a curated set of references designed to efficiently teach you the core mathematical concepts that underpin the foundations of GNNs. This section is self-directed and fully available at the time of enrollment.

The Hands-on section is a synchronous experience over a 6-week period in which a cohort of students work on the same material and help one another through e.g., code reviews. The content consists of model-building code exercises, screencasts that walk you through the solutions, and an additional set of lecture-style videos that frame the ML tasks and discuss practical problems of building GNNs, like scalability and over-smoothing. See the FAQ below for the Hands-On course schedule.

It's expected that students of this course participate in community activities, such as discussions in chat and peer-to-peer code reviews. All participants will receive access to a course GitHub organization where course and student code will be hosted, and a private Discord room where Zak and students will discuss course material and engage with the community.

This course assumes basic familiarity with GitHub and building ML models in PyTorch. Please see the component course pages for additional details.

In addition to this core content, there is also a set of bonus videos that discuss cutting edge GNN research and applications with the original authors. For example, we dive into work from Deep Mind with Jonathan Godwin on how they used GNNs to model complex physics simulators, and discuss industry applications with scientists from Twitter and Uber.