Principles and application of Machine Learning in Geosciences
- About the workshop
Machine learning is a branch of artificial intelligence that is based on the idea that computer systems can learn from data, identify patterns and make decisions with minimal human intervention. Machine learning is increasingly attracting the attention of academia, industry, government and other organisations where it is generally used to analyse large and generally complex datasets, commonly referred to as “big data” to make reliable statistical predictions.
In this workshop we will demonstrate principles of machine learning, application of mathematical and statistical algorithms in machine learning and the role of machine learning in geosciences with special emphasis to exploration of mineral deposits. A practical case study will be presented to aid the understanding. We will also provide some technical guidelines on the use of some of the commonly used open-source technologies.
- Who is it for?
The workshop is aimed at 4th year, post-graduate students and industry professionals in the fields of mining and exploration geology, geophysics and computational sciences.
Saturday 5th October 2019
8am to 5pm
R1 550 per person (full day)
Student cost: R630 (full day)
Lunch, morning and afternoon tea included
Glen Nwaila (UNIVERSITY OF THE WITWATERSRAND)
Biography: Dr. Glen Nwaila is a geoscientist with multi-disciplinary experience in geology and process engineering. His formal education includes various qualifications that span: BSc (Hons.) in Geology, MSc in Chemical Engineering, N.Dip. Courses in Analytical Chemistry and Metallurgy and a Dr. rer. nat. in Geology (Magna Cum Laude). He was appointed lecturer of economic geology in January 2018 at the University of the Witwatersrand. In this role, he teaches economic geology to both under- and post-graduate students. Additionally, Glen supervises post-graduate students on multi-disciplinary projects that involves geology, geophysics and process engineering. He currently serves as a member of the Reference Group in the Water Research Commission. In his time with the University, Glen has been a strong advocate for machine learning and numerical modelling in geosciences. Prior to joining the University of the Witwatersrand, Glen has worked for 10 years in various consulting and mining companies. He received the SA rising star award in mining and metals in 2014 and many best-achiever awards from various private companies and universities.
Email address: Glen.Nwaila@wits.ac.za
Phumlani Khoza (UNIVERSITY OF THE WITWATERSRAND)
Biography: Phumlani Nhlanganiso Khoza is an inter-disciplinary scientist with broad interests that converge to applied research in analytics and its extension to business. His formal education includes various qualifications that span: Physics, Computer Science, and Computational & Applied Mathematics. At present, he is an Associate Lecturer in the School of Computer Science & Applied Mathematics at the University of the Witwatersrand, where he is also working towards obtaining a PhD in Computer Science. His overarching research interest pertains to developing conceptual and technological tools to probe complex systems, especially those involving human behaviour.
Email address: firstname.lastname@example.org