(PDF) Call for papers, CAA 2020, Oxford. Session 5: Machine learning in archaeological research; challenges and opportunities

After the success of last year's session on Machine Learning (ML) and the fruitful discussion that followed, it became apparent that there is plenty of interest in the application of these methods in archaeology. This interest might be partly ascribed to advances made in Deep Learning-in particular Convolution Neural Networks-across various disciplines. Applications using these methods now show high performance and in some cases exceed humans on challenging tasks ranging from computer vision to natural language processing. In digital archaeology we have seen and foresee applications of these techniques including automated object detection in remote sensing data, artefact image classification, use-wear analysis, text mining, paleography, predictive modelling, 3D shape analysis and recognition, and typology development. This session aims to: 1) offer a space for comparing methods, algorithms, code, APIs and workflows; 2) discuss the problems related to their application and; 3) offer insights into best practices including sources of error and validation methods.