Graph Powered Machine Learning (2020)

Welcome to Graph Powered Machine Learning


Description:

Many powerful Machine Learning algorithms are based on graphs, e.g., Page Rank (Pregel), Recommendation Engines (collaborative 􏰄ltering), 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 therefore investigate the intersection of graphs and Machine Learning and cover 3 parts:

(1) Graph-based Feature Engineering and Graph Algorithms:
We will start by looking at popular graph algorithm and their value for feature engineering for Machine Learning models.

(2) Graph Embeddings and Graph Neural Networks:
Utilizing graphs as input to Neural Networks is a very powerful combination. In this part we will consider different Embedding strategies and the 􏰄eld of Graph Neural Networks.

(3) Machine Learning Metadata:
We know about the value of high quality and quantity for building high quality machine learning models, but for operating a production grade machine learning pipeline Metadata is equally important. In this part we will look at leveraging graphs to capture metadata and provenance information of our machine learning ecosystem.

Prerequities: 

- A general background in computer science 
- Familiarity with database management at undergraduate level
- Participants who have taken a graduate database course will bene􏰄t from this additional background.

Learning Objectives: 

The goal of this course is for students to familiarize themselves with recent algorithmic and methodological advancements in the area of graph powered machine learning. Particular focus is given to highlighting how the areas of graph data management and machine learning can bene􏰄t each other.

Students will in particular deepen their knowledge of state-of-the-art popular graph algorithms, machine learning over graphs, and metadata management for machine learning pipelines. Researchers working with (large-scale) graph data will 􏰄nd inspiration and tools for further improving their approaches and systems.

Participants at the end of the course should be able to:
- Describe and explain the main methods of graph empowered machine learning
- Compare and assess the limitations and advantages of the different methods
- Understand the opportunities offered in terms of research and industrial application by graph powered machine learning


Organizer: Professor Katja Hose

Lecturers: Jörg Schad, ArangoDB, Germany


ECTS: 2

Time: September 21-22, 2020

Place: TBA (this course will take place with physical attendance)

Zip code: 

City: 

Number of seats: 30

Deadline: September 4, 2020

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.