250124 VO Graph Algorithms and Machine Learning (2023S)

The course will be an introduction to graph representation learning.
It will begin with classical methods to understand graphical data for examples:
1. Random walks or graph Laplacians to understand concrete algorithms such as PageRank.
2. Graph neighborhood measures to create loss functions used in Machine Learning on graphs.

With this, we will discuss the theoretical and practical aspects of Graph Neural Networks (GNN), emphasizing exceptional cases such as Message Passing Algorithms.

Prerequisites:
Basics of ML
Stochastic and probability theory basics
Linear Algebra
Experience with Python helpful