Engineering Pattern Recognition

Code to reproduce paper results (or as close as possible, depending on data-availability) is available via Github. Each publication has a Jupyter notebook. Scripts are provided to test and demonstrate the ‘EPR’ module.

So far, engineering applications of:

• Active learning: inspection management for monitoring (generative mixture models)
• Semi-supervised learning: combining labelled and unlabelled data (generative mixture models)
• Transfer learning: sharing data/models between systems (TCA)