Graph-based Machine Learning
Jun.-Prof. Dr.-Ing. Alexander Dockhorn
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General
Supervisor:
Appointments (every summer semester, including lecture and exercise classes: 5 CPs):
- Lecture: see
- Exercise: see
Exam:
- Written exam
- Appointments: will be announced at the beginning of the summer semester
Lecture slides: see
Exercise materials: see
Content of the lecture
In this cource, we will take a look at fundamental concepts and methods of graph-based Machine Learning and related methods for decision support.
You will learn about exemplary applications of graph-based models and understand how they work.
Furthermore, you will learn to select, adapt, and evaluate models for new applications and how to evaluate them.
Topics
- Introduction to Graphs, Types of Graphs, Disease Spread/Social Networks, and other Applications
- Basics of Probability Theory, Bayes Theorem, Representation of Uncertain Information
- Independence, Decomposition, Bayes Networks
- Probabilistic Reasoning, Propagation, Naive Bayes
- Parameter Learning, Structure Learning
- Causal Networks
- Graph Clustering, Random Walks, Node2Vec
- Graph Neural Networks
- Frequent (Sub-)Graph Mining
Requirements
We strongly recommend that you know the foundations of
- machine learning
- deep learning
- or AI in general
in order to attend the course. You should have attended at least one other course for ML and DL in the past.
This course as well as the exercises will be in English only.
Literature
- Borgelt, C., Steinbrecher, M., and Kruse, R. (2009). Graphical Models: Representations for Learning, Reasoning and Data Mining (2nd Edition). John Wiley & Sons, Chichester, United Kingdom
- Jensen, F (1996). An Introduction to Bayesian Networks. UCL Press, London, United Kingdom
- Wu, L., Cui, P., Pei, J., and Zhao, L. (2022). Graph Neural Networks - Foundations, Frontiers, and Applications. Springer, Singapore, 2022, https://graph-neural-networks.github.io/index.html
