Projects
The AI Association conducts educational and exploratory projects that enable students and interested members to learn artificial intelligence and machine learning through hands-on collaboration.
ML4Mensa
In this project, members of the AI Association collaboratively explored how machine learning methods can be applied to real-world tabular data. Using anonymised historical cafeteria transaction data provided by the university services, team members learned and applied techniques for data preprocessing, feature engineering, the comparison and evaluation of different machine learning models, and the interpretation of model behaviour and limitations.
The next figure shows the predictions of the final ML model compared to the ground truth data:
Media coverage:
- Bayreuther Tagblatt (in German)
- Kurier (in German)
- inFranken.de (in German)
- UBT press release in English and in German.
Contributors:
- Pascal Fechner
- Nico Höllerich
- Renato Mio
- Felicitas Feick
- Pascal Lange
- Mohammad Ahmad
This project is in progress.
Coffee consumption forecasting
This project focused on learning sequential modelling techniques using time series data. Based on historical sales data of hot beverages from the university cafeteria, members jointly investigated: how time-dependent data can be structured for machine learning, the use of recurrent neural networks (such as LSTM models), and how different modelling assumptions influence predictions.
Contributors:
- Ivan Khrop
- Pascal Fechner
- Andreas Karasenko
- Felicitas Feick
- Renato Mio
- Gianluca Pani Casanova
- Franz Erhard
- Henri Berntgen
- Frederick Hornung
- Nico Höllerich
- Tom Zeilmann
This project was concluded and is currently closed.