Machine learning on Graphs course: Pre-requisites

(This is part of a four part course hosted by Octavian.ai this summer)


Welcome intrepid traveller! This is the start of Octavian’s Machine learning on Graphs course. Over the summer we’ll cover a wide range of different approaches to machine learning on graphs. To get the most out of the course, it’ll help to have a firm grounding in TensorFlow and Graphs. In this article I will list some skills it’ll be good to have, and helpful resources for learning them.

Some of these topics are optional bonus material, for enthusiastic/advanced students. I’ll call those out with a BONUS tag. I promise they’ll be interesting to learn for their own sake :)


Neural Networks basics

The course will assume you’re familiar with Neural Networks, how they work and why we use them:

Graphs

We’ll talk a lot about graphs — and by graphs we mean connected data, as opposed to charts in PowerPoint slides.

TensorFlow

You’ll want to be familiar with TensorFlow and how build models in it. TensorFlow themselves have published a lot of great learning materials for you to use.

Further machine learning topics