An Introduction to Machine Learning – University of Leeds

    Published on April 10th, 2021

    CSA Global Resource Consultant Adrian Martinez and Senior Consultant Antoine Cate from SRK will present on “An Introduction to Machine Learning” hosted by the University of Leeds between Wednesday 21 to Thursday 22 April 2021.

    Machine learning is a collection of computer algorithms that are used to solve problems with minimum human input. The technology is quickly transforming the mining industry, improving efficiency, reducing workloads and providing more accurate geological interpretations. This has allowed for the generation of exploration targets, developments in automated mining and much more.

    The workshop endorsed by the European Federation of Geologists is a comprehensive introduction to machine learning technology to those in the mining industry.  Participants will be exposed to the theory of machine learning and will explore the technology’s concepts and uses. This will include a practical exercise, where participants will learn to produce a basic model and learn to interpret machine learning data.

    Learning Outcomes

    The workshop will encompass the theory of machine learning, exploring the concepts and uses of the technology. This will include a practical exercise, where participants will learn to produce a basic model and learn to interpret machine learning data.

    Completion and attendance of the course will count towards CPD hours for Euro geologists.

    Course Delivery

    The course will run for 4 hours and delivered over two-hour slots on the 21st and 22nd of April between 15:00 and 17:00 BST each day.

    Course Content

    • Theory
    • Case studies
    • Prospectivity mapping 2D
    • Alteration mapping using satellite imagery
    • Logging with text classification
    • Domain modelling using time series
    • Resource classification clustering
    • Pebbles classification CNN
    • Vein classification CNN
    • GAN for geophysical

    • Exercise:
    – Basalt-rhyolite supervise Classification

    Objectives

    Upon completion of the course, participants will have the foundational understating of machine learning, which can then be further developed to allow for the technology to be utilised. This course is aimed at university students and industry professionals and is open to the general public.

    Requirements

    Laptop with 64 bits Windows, Mac OX or Linux, internet connection, and administrative rights to the computer.

    REGISTER HERE

    To register, participants will have to make a University of Leeds Union guest account.

    Cost

    • University of Leeds Society of Economic Geologists members: free
    • Industry professionals: £25
    • Euro Geologists: £20
    • Other university society of economic geology members: £10

    ABOUT THE PRESENTERS

    Dr. Adrian Martinez PhD, CFSG, BS, APEGBC, P.Geo.Adrian Martinez
    Resource Consultant – CSA Global
    Adrian has 16 years of experience as a consultant on resource estimation and technical reports, operational auditing, due diligence and technical risk analysis, mine geology, sampling and geological interpretation. He has worked with various commodities such as gold, copper, nickel, submarine sills, barite, clay and limestone deposits. Some examples of Canadian and international mineral resource estimation projects where he worked are Coringa, Cow Mountain, Borden Gold Property, Gallowai Bull River Mine and Brucejack. He also worked on Cuban projects in Moa Bay, Cementos Mariel, Oro Barita and Merceditas.

    Antoine Cate, Structural Geologist - SRKAntoine Cate
    Structural Geologist – SRK
    Antoine is a specialist in data science and machine learning. He is experienced in mineral exploration, structural geology, geochemistry, and 3D modelling. Antoine Caté has field experience in mineral exploration on a number of deposit types (VMS, MVT, orogenic, epithermal), as well as a strong academic and professional background in processing and interpreting complex geoscientific data. He has a strong and recognized expertise in the applications of data science and machine learning in mineral exploration. Antoine uses various geoscience software (Leapfrog, ArcGIS, QGIS, ioGAS) and is experienced in the Python programming language.

    Training course endorsed by the European Federation of Geologists

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