At the end of this course, the student will learn:

• the basics of graph theory and its application to machine learning
• the theory behind Graph Neural Networks (GNNs) and their various architectures, including GCNs and GATs
• real-world applications of GNNs in fields such as social networks, bioinformatics, and recommendation systems
• designing, implementing, and evaluating GNN models using popular deep learning frameworks