Overview
Introduction to graph machine learning and graph neural networks
From graph theory to graph learning techniques
First iteration of the course delivered in Jan-Apr 2023

Lecture 1 - Introduction to Graph Machine Learning
Material: Slides
GitHub: Course Repository
Installation: Instructions for running the course notebooks

Lecture 2 - Introduction to Graph Science
Material: Slides
GitHub: Code

Lecture 3 - Graph Clustering
Material: Slides
GitHub: Code

Lecture 4 - Graph SVM
Material: Slides
GitHub: Code

Lecture 5 - Recommendation on Graphs
Material: Slides
GitHub: Code

Lecture 6 - Graph-based Visualization
Material: Slides
GitHub: Code

Overview
Introduction to deep learning

Lecture 7 - Attention Neural Networks
Material: Slides
GitHub: Course Repository

Lecture 8 - Diffusion Models
Material: Slides
GitHub: Course Repository

Overview
Introduction to machine learning

Lecture 3 - Vanilla kNN, k-d Tree, Decision Tree, Random Forest and Gradient Boosting
Material: Slides

Lecture 4 - Linear Models and Support Vector Machine
Material: Slides

Lecture 5 - Variance and Bias
Material: Slides

Lecture 6 - Regularization Techniques
Material: Slides