Resource Registry: Machine learning

This research project explores the application of AI and machine learning technqiues in video games, focusing on procedural content generation and AI-driven advice. The research team (RAIL) aims to dynamically generate diverse game content and create intelligent virtual companions that offer personalised guidance and strategies to enhance player experiences. By integrating AI technologies, the research team strives to revolutionise video game desgin and contribte to the advancement of AI in the gaming industry.

This research group spans the University of Johannesburg with staff, students and postdoctoral researchers working in different areas that apply AI and Machine Learning. Their research areas include: Telecommunications, power-line communications, smart grid, and visible light communications, Machine learning, Artificial Intelligence, Data science, Renewable Energy, Power Systems, Power Engineering, Power Distribution and Generation.

Using Machine Learning and AI to predict financial distress of African start-ups. Project is run by the FinTech Hub at Wits.

In this line of work, the research team is interested in techniques that allow an agent to quickly leverage past knowledge to solve new tasks. In particular, the research team focuses on how agents can acquire behaviours that can be combined to generate interesting, novel abilities. One particular focus is applying Boolean operators to learned behaviours to generate probably optimal solutions to new tasks. These approaches are not only human-understandable but also result in a combinatorial explosion in an agent’s abilities, which is key to tackling the multitasking or lifelong learning setting.

The Computational Intelligence Research Group (CIRG) focuses on various aspects of fundamental and applied computational intelligence and machine learning research. Nature-inspired algorithms such as artificial neural networks, deep learning methods, self-organizing maps, particle swarm optimization and evolutionary computing are studied both theoretically and practically. Real-life applications include computer vision, data mining and anomaly detection.

The research group focuses on data-related research and development within a cross-disciplinary space. The research group aims to develop and advance methodologies, algorithms, tools and techniques in a holistic manner within the field of data science by working in the following areas: Data Wrangling, Data Mining, Data Analysis and Visualisation, Data Engineering, Data Science, Machine Learning Engineering, Fairness, Accuracy, Confidentiality, Transparency, Security (FACTS) and Discrimination in Data.

Our general areas of work straddle Data Science for Society as well as Local Language Natural Language Processing. These two strands are complementary. Our work in Data Science and Society has allowed us to have a more nuanced approach to understanding the systematic challenges that face being able to do excellent science with local languages. Through Data Science for Society, we have to understand how when one carries through Data Science research, we situate how the users are part of the process. We find that we need to adjust our research to take care of these challenges and innovate in ways we gather direct data or alternative data.

For us, Data Science for Society means being able to improve approaches/methods or scientific tools for DS while enhancing the ways decision-makers can use the insights that come from these tools. Local Language Natural Language Processing is focused on ways to develop new tools, new data and methodology to improve the state of African languages.

The DigiForS research group undertakes research in Digital Forensic Science. DigiForS’s main objective is to find innovative security solutions for state-of-the-art technologies. This includes finding solutions that will help to make emerging technologies safe for use, using security technologies to enable new applications, find solutions that will better protect existing technology, as well as extracting forensically relevant information.

Using Machine Learning and AI to predict the success of Mergers and Acquisitions. Project is run by the FinTech Hub at Wits.

The NICOG research group focuses on solving real-world problems using machine learning and optimisation techniques that take analogies from nature such as genetic algorithms and neural networks.

The Robotics, Autonomous Intelligence and Learning (RAIL) is one of the largest AI research labs in the southern hemisphere, ensuring African involvement in Artificial Intelligence, Machine Learning, and Robotics.

The Fintech Hub in the Wits School of Economics and Finance brings academia and industry together to drive innovation and advancement in the financial technology space and to equip students with knowledge of finance and technologies such as blockchain, cryptocurrencies, data analytics and machine learning that are essential for navigating the future of finance.