Elevating data analytics and machine learning in South Africa: Advancing capacity and statistics and machine learning through focused and collaborative research initiatives

Machine Learning in support of Computational and Theoretical Sciences

The goal of the programme is to strengthen machine learning (ML) collaboration in NITheCS in two ways:

ML research: development of new, specialised machine learning techniques.

ML as a tool: applying machine learning for scientific modelling applications.

Principal Investigators: Marelie Davel (North-West University), Stefan Lotz (South African National Space Agency)

• Marelie Davel (PI), North-West University
• Stefan Lotz (PI), South African National Space Agency
• Cleo Conacher, Stellenbosch University
• Thipe Modipa, University of Limpopo
• Deshen Moodley, University of Cape Town
• Chris Oosthuizen, University of Cape Town
• Simon Ramalepe, University of Limpopo
• Stefan Schoombie, University of Cape Town
• Jonathan Shock, University of Cape Town
• Ilya Sinayskiy, University of KwaZulu-Natal
• Bruce Watson, Stellenbosch University

Knowledge Discovery in Time Series Data

Machine learning techniques play an increasing role in assisting scientists and engineers in knowledge discovery: obtaining novel information from large, possibly complex data sets. Many practically important tasks, such as weather prediction, financial forecasting, or speech processing, are modelled using time series information. Knowledge discovery in time series data is an active field of research, with techniques such as feature attribution used to gain new insights into the underlying processes being modelled.

Machine Learning Forum

The programme aims to grow a forum for cross-cutting projects executed in other NITheCS focus areas, where those projects rely on machine learning expertise.

The programme is open to all NITheCS Associates who currently work in machine learning, or are interested in ML approaches in their research.