Machine Learning (ML) in geoscience has left the realm of curiosity; the mining and mineral exploration industries have adopted the advances that the field provides, from exploration through to ore body knowledge, mine planning and operations.

Join us to gain insight into core ML concepts in the context of geoscience. Gain an understanding of the possibilities and pitfalls of applied ML via a mix of theory, case-studies and first-hand accounts from industry and external guest-speakers.

This 2-day short course aims to introduce geoscientists to some of the powerful tools and

techniques that exist within data science and machine learning. By the end of the course participants will have the confidence to identify aspects of you or your teams’ geoscientific workflow that can be augmented by relevant ML technologies, while being more aware of common ML fallacies in the context of geoscience. The course includes theory and practical applications of:

● Foundational theory of Machine Learning algorithms for clustering, dimensionality

reduction and supervised classification

● An understanding of how these techniques can be applied to geoscientific problems

within exploration and mining.

● Ask questions of any machine learning solution to help better understand it’s utility and

limitations.

● Ability to use Orange Data Mining/R/Python to:

          ○ Prepare, filter and manipulate data

          ○ Analyse/explore multivariate data using clustering and dimensionality reduction

          ○ Build simple supervised classification models

We won't presume any prior knowledge for the course, but it can be beneficial to have used Orange Data Mining (the software we do the practical in) prior to the course to familiarise yourself with the basics. There is ample time for questions during the class.