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Machine Intelligence and Learning from Experience (MILE)

Towards the realization of Artificial Intelligence (AI), the capability of a machine to reason in complex environments plays an essential role. Recent advances in data-intensive machine-learning methods and computing power have made it possible to build machines that can learn to reason, which finds its application in science, technology and commerce including, but not limited to, health care, manufacturing, education, financial modeling, policing, and marketing.

The Machine Intelligence and Learning from Experience (MILE) focus area is mainly concerned with the theoretical and practical principles of artificial intelligence and machine learning and their practical applications to real-world problems. In a broader concept, the ultimate goal of the MILE focus group is to build machines which are capable of performing tasks and cognitive functions in a complex environment at a level of human intelligence. To achieve this goal, computational structures that can learn from their experiences to aid reasoning process must be designed and implemented in the form of computer program based on theoretical and practical principles of machine learning theory.


Ritesh Ajoodha (Wits) Focus Area Coordinator (FAC)
Anban Pillay (UKZN)
Andrew Paskaramoorthy (Wits)
Andries Engelbrecht (SU)
Andriette Bekker (UP)
Benjamin Rosman (Wits)
Bruce Bassett (UCT)
Bruce Mellado (Wits)
Charles Chimedza (Wits)
Clint van Alten (Wits)
Corné van Daalen (SU)
Dustin van der Haar (UJ)
Ebrahim Momoniat (UJ)
Eustasius Musenge (Wits)
Frans Kanfer (UP)
Hairong Bau (Wits)
Herman Engelbrecht (SU)
Herman Kamper (SU)
Hima Vadapalli (Wits)
Ken Nixon (Wits)
Maria Schuld (UKZN)
Maria Vivien Visaya (UJ)
Pieter de Villiers (UP)
Pravesh Ranchod (Wits)
Richard Klein (Wits)
Ritesh Ajoodha (Wits)
Scott Hazelhurst (Wits)
Simukai Utete (AIMS)
Steve Kroon (SU)
Steven James (Wits)
Terence van Zyl (UJ)
Tim Gebbie (UCT)
Turgay Celik
Vukosi Marivate (UP)
Willie Brink (SU)