Improving Resource Estimation with Machine Learning

The proposed project aims to improve the prediction of mineral resources for better decision making throughout a mining project, that is, to decide whether to reject or to process extracted material using machine learning algorithms. This will help maximize profit for mining companies while minimizing environmental impact as the correct material will be processed more often. The use of machine learning algorithms has become popular recently due to the ability to learn important features from a large amount of data. This will be used to add financial value to mining projects by correctly classifying material mined.

Faculty Supervisor:

Jeff Boisvert


Camilla Zacche da Silva


Teck Resources Limited


Engineering - civil



University of Alberta



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