Machine Learning applied to geosciences and mining

• Define geological and geometallurgical domains with unsupervised learning, and assess the quality of the clusters you obtain.
• Build, validate and tune supervised models, and know when a model is fit to be applied.
• Predict grades and lithology with neural networks, using Deep Kriging.
• Run the full workflow from Python and scikit-learn into Isatis.neo.

This course assumes you know your geology and your geostatistics, and teaches you machine learning.

It suits resource geologists and geomodelers who define domains and estimate grades; exploration geologists working on lithology classification and targeting; geometallurgists building domain models from process data; geostatisticians looking to extend their methods beyond kriging; and consultants, researchers, and postgraduate students working on mineral resource problems.

• Balanced learning approach. Theory paired with practical applications, so you understand and apply the concepts.
• Hands-on software training. Computer exercises in Isatis.neo, on real datasets.
• Personalized feedback. Individual guidance from experienced trainers throughout the online sessions.
• Comprehensive resources. A temporary software license, course documentation, journal files, and datasets to keep and reuse after the course.
• Certificate of completion issued at the end of the session.

You need a working grasp of statistics, algebra, and geostatistics. Python is welcome but not required; the course begins with an introduction to the language.