CSA Global is pleased to present:
A two-day course on:
An Introduction to Machine Learning
on
Tuesday and Wednesday 28-29 January (Vancouver)
Tuesday and Wednesday 25-26 February (Vancouver)
Tuesday and Wednesday 28-29 April (Vancouver)
and
Tuesday and Wednesday 11-12 February (Toronto)
Tuesday and Wednesday 14-15 April (Toronto)
PRESENTED BY: Resource Consultant, Adrian Martinez
This two-day training course is designed for geologists, geophysicists and professionals working in the mining industry. A strong background in mathematics or previous knowledge of machine learning is not required to undertake this course. If your role is to make sense of your data or to automatize tedious tasks, such as core logging, then this training course is for entirely for you.
Upon completion of this course, you will be able to:
*Understand what machine learning is and what is it used for;
*Identify different types of software to conduct machine learning;
*Recognise general concepts and workflows;
*Learn how to select meaningful features from a database;
*Become familiar with data preparation and data issues;
*Acquire skills to train, validate and test machine learning algortihms;
*Understand large groups of machine learning techniques for classification and regression; and
*Interpret results and how to select and create meaningful dataset variables.
ABOUT OUR PRESENTER
DR ADRIAN MARTINEZ
Adrian is both a geologist and a geostatistician. He produces open source software for geostatistics and mineral resources in Python, Fortran, Cython, C and SQL. He has worked as a consultant since 2002 covering many commodities including gold, copper, nickel, chromium, and raw material for cement industry. Adrian has considerable experience using multiple indicator kriging for resource estimation of gold deposits with high nugget and domaining issues; with non-linear geostatistics and with conditional simulations for resource estimation and model validation. Adrian has previously worked as an Assistant Professor in Cuba and Ethiopia, teaching geology and geostatistics.