Course Abstract

Training duration : 90 minutes

Machine Learning and Graph Processing (e.g., Knowledge Graphs) have been two of the main trends over the past years. Many powerful Machine Learning algorithms are based on graphs, e.g., Page Rank (Pregel), Recommendation Engines (collaborative filtering), text summarization and other NLP tasks. There are even more applications once we consider data pre-processing and feature engineering which are both vital tasks in Machine Learning Pipelines. In this course we will consider the symbiosis of graphs and Machine Learning.


Learning Objectives

  • Understand Graph-based Feature Engineering and Graph Algorithms

  • Understand Graph Embeddings and Graph Neural Networks

  • Text length of individual points can be shorter or longer depending on your needs


Instructor Bio:

Head Of Engineering and Machine Learning | ArangoDB

Jörg Schad, PhD

Jörg Schad is Head of Development and Machine Learning at ArangoDB. In a previous life, he has worked on or built machine learning pipelines in healthcare, distributed systems at Mesosphere, and in-memory databases. He received his Ph.D. for research around distributed databases and data analytics. He is a frequent speaker at meetups, international conferences, and lecture halls.

Course Outline

Module 1: Graph-based Feature Engineering and Graph Algorithms 

- Popular graph algorithm 

- Value for feature engineering for Machine Learning models 

Module 2: Graph Embeddings and Graph Neural Networks 

- Utilizing graphs as input to Neural Networks In this part 

- Different Embedding strategies 

- The field of Graph Neural Networks Module 

3: Graph-based Machine Learning Metadata 

- Value of high quality and quantity for building high-quality machine learning models 

- Operating a production-grade machine learning pipeline ametadata 

- Leveraging graphs to capture metadata and provenance information of machine learning ecosystem.

Background knowledge

  • This course is for current and aspiring Data Scientists, Machine Learning Engineers and Graph Theory Practitioners

  • Knowledge of following tools and concepts is useful

  • Jupyter/Colab notebook

  • Hosted Databases

  • Machine Learning Frameworks

Real-world applications

  • Graph powered machine learning is used IoT applications generating continuous stream of data

  • Graph ML has various applications in telecom, supply chain and logistics industries

  • Graph models are being increasingly deployed for credit-card fraud detections