Machine Learning applied to geosciences and mining | Training course

Learn the concepts and practices of Machine Learning applied to the mining industry to aid decision-making at every phase of mineral resource modeling.

Objectives

The course is designed to help you understand Machine Learning techniques, enabling you to develop routines for:
– Defining geological or geometallurgical domains
– Applying classification algorithms in mining applications
– Utilizing regression algorithms in mining applications
– Considering practical implementations
– Integrating Sklearn and Isatis.neo

Outlines

  • Half of the course is devoted to methodological presentations, and the second half to practical exercises to deepen the understanding of concepts. The focus is on illustrations and the practical contribution of the covered concepts.
  • Computer exercises with Isatis.neo Mining Edition.
  • Course material and temporary software license provided.

Who should attend

This course targets professionals seeking to gain both theoretical and practical knowledge of Machine Learning and its applications in geosciences and the mining industry.

Course content

  • Module I: General aspects of Machine Learning and introduction to the Python language.
  • Module II: Unsupervised learning
    Data transformations, clustering techniques – theory and practice, cluster quality evaluation.
  • Module III: Supervised learning
    Predictive models – theory and practice, model validation, hyper-parameter tuning, model application. 

** The course can be reduced to two days by removing Module II or Module III from the program.

Prerequisites

Basic knowledge of statistics, algebra, and geostatistics is recommended. Familiarity with Python is optional.