Machine Learning applied to geosciences and mining
Learn the concepts and practice of Machine Learning applied to the mining industry to aid decision-making at every phase of mineral resource modeling.
The course aims to familiarize you with Machine Learning techniques so that you will be able to build routines for:
− Optimizing processes by automating lithological classification;
− Definition of geological or geometallurgical domains;
− Advanced exploratory analysis of multivariate databases;
− Adding missing information to heterotopic databases.
- 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 is aimed at professionals who want to acquire theoretical and practical knowledge about Machine Learning and its applications to geosciences and the mining industry.
- Module I: General aspects of Machine Learning and introduction to the Python language.
- Module II: Unsupervised learning: clustering techniques and data transformations.
- Module III: Supervised learning: predictive models and process optimization in geosciences.
** The course can be reduced to two days by removing Module II or Module III from the program.
Previous knowledge in statistics, basic geostatistics, and geological modeling is recommended. Basic knowledge of Python is not required.