Student who passed the course satisfactorily will be able to:

• understand the principles of graph theory and how it applies to machine learning
• design and implement Graph Neural Networks, including GCNs, GATs, and other advanced architectures
• apply GNNs to solve real-world problems, such as social network analysis and drug discovery
• evaluate the performance of GNN models and compare them to traditional machine learning techniques