Machine learning with fossil data: analyzing environmental and climate change

A tutorial to be presented at ECMLPKDD'17, Skopje on Friday, September 22, 2017


Presenter: Indrė Žliobaitė

Abstract: Global fossil databases have been growing rapidly in the last decade. They aggregate and accumulate findings and knowledge that palaeobiologists acquired over many years. These datasets are big data in their essence - compiled from different sources, to an extent subjective, include specific biases and uncertainties, data sparseness and quality varies over time and space. In addition, to understand relations between organisms and climate high volume and large velocity satellite observations some into play that require scalability in computing. Databases of this kind offer an excellent ground for interdisciplinary machine learning research. This tutorial will outline research questions that could be addressed using computational methods, discuss characteristics of fossil data and computational tasks for machine learning and data mining, overview existing computational approaches, and discuss what more could be done from the machine learning and data mining perspective.