10:00 - 13:00
Many powerful machine learning algorithms—including PageRank (Pregel), recommendation engines (collaborative filtering), and text summarization and other NLP tasks—are based on graphs. And there are even more applications once you consider data preprocessing and feature engineering, which are both vital tasks in machine learning pipelines.
In this workshop, you will learn about Graph Powered Machine Learning starting with simple graph algorithms, over graph analytics, up to Graph Neural Networks.
In particular, the workshop will cover the following topics:
– Why graphs are such a powerful abstraction
– What use-cases are suitable for Graph-based Machine Learning
– How to leverage knowledge graphs
– The graph ecosystem including its many many powerful open source tools
– How to extract value from graphs using graph analytics and graph algorithms
– How to combine deep learning and graphs
– How we can learn graphs features using graph neural networks