Build competency aware and assuring machine learning systems. Recent development in machine learning (ML) has seen ML models with extremely high prediction accuracy. However, to support human-machine partnership in decision-making in complex environments, beyond accuracy, it is essential for ML systems to be competency aware and reliable, and at the same time be exploratory. This project aims to develop novel techniques to equip a ML system with the ability to identify own competency, to justify ....Build competency aware and assuring machine learning systems. Recent development in machine learning (ML) has seen ML models with extremely high prediction accuracy. However, to support human-machine partnership in decision-making in complex environments, beyond accuracy, it is essential for ML systems to be competency aware and reliable, and at the same time be exploratory. This project aims to develop novel techniques to equip a ML system with the ability to identify own competency, to justify its competency and decisions, to explore unknown situations and fully utilise existing expertise to deal with unknowns. The expected outcomes of the project will enable ML systems to become truely intelligent and reliable machine partners for human decision makers in a wide range of applications.Read moreRead less
Towards knowledge discovery from imperfect and evolving data. Information extraction from data is critical, both to analyse and protect consumer data. However, many learning techniques are developed using perfect, static datasets, quite different to messy, ever-changing real-world data. This project aims to develop data analytics techniques that can extract accurate information in complex structures from imperfect/incomplete data that changes over time. Expected outcomes are a prototype tool, te ....Towards knowledge discovery from imperfect and evolving data. Information extraction from data is critical, both to analyse and protect consumer data. However, many learning techniques are developed using perfect, static datasets, quite different to messy, ever-changing real-world data. This project aims to develop data analytics techniques that can extract accurate information in complex structures from imperfect/incomplete data that changes over time. Expected outcomes are a prototype tool, tested on real datasets, that combines new techniques in data modelling, algorithm development, and system design. Likely benefits are enhanced Australia's competence in data science through student training and new, robust data tools relevant to critical sectors such as cybersecurity, healthcare, and defence.Read moreRead less