This repository contains a collection of exercises focused on machine learning techniques for graphs and sequential data. The exercises cover a range of topics relevant to machine learning for graphs and sequential data.
To get started, simply clone or download this repository and navigate to the exercise you are interested in. Each exercise includes a dataset and the necessary files to run the machine learning models. The files include my work based on the summer semester course: Machine Learning for Graphs and Sequential Data, Technical University Munich.
To run any of the projects, navigate towards the exercise you are interested in and download the requirements:
pip install -r requirements.txt
The included projects cover a range of topics relevant to machine learning for graphs and sequential data, including:
- Hidden markov models
- Graph neural networks
- Time series analysis
- Sequence prediction
- Recurrent neural networks
- Long short-term memory (LSTM) networks
- And more!