Adjustment of classifiers after the context changes

Machine learning and data mining
Machine learning models are often very sensitive to the context and can fail badly if there is even a slight change in context. Suppose you use the photos that you took this summer and train a model to classify hair colour of the people in the photos. You now go and apply the model on the photos this autumn and realise that it wrongly predicts too many people as brunette. You study what the problem is and realise that the summer photos were taken outside in the sun whereas the new autumn photos are mostly indoors and dark.

The goal of this project is to use some standard machine learning datasets and try out a simple procedure to adjust the model to the new context. This procedure modifies the predictions such that in the new context the distribution of predicted classes is the same as in the training data. The adjustment methods have been recently invented by the supervisor in a theoretical paper and the aim is to show their usefulness in practice. This project has the advantage of working with simple methods while being very relevant to the state of the art in machine learning. This project can be extended to a bachelor or master thesis.
Graduation Theses defence year
Meelis Kull
Spoken language (s)
Estonian, English
Requirements for candidates
Bachelor, Masters

Application of contact

Meelis Kull