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Research Topic : learning
Australian State/Territory : VIC
Field of Research : Pattern Recognition and Data Mining
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  • Active Funded Activity

    Discovery Projects - Grant ID: DP210102798

    Funder
    Australian Research Council
    Funding Amount
    $361,000.00
    Summary
    A Generic Framework for Verifying Machine Learning Algorithms. This project aims to discover new ways to verify whether decisions made by Artificial Intelligence and Machine Learning algorithms are as per the specifications set by their designers and/or regulatory bodies. The project also provides new methods to align algorithm decisions when they are found to be non-abiding. The outcomes will include new machine learning theories and frameworks for algorithmic assurance. The significance of the .... A Generic Framework for Verifying Machine Learning Algorithms. This project aims to discover new ways to verify whether decisions made by Artificial Intelligence and Machine Learning algorithms are as per the specifications set by their designers and/or regulatory bodies. The project also provides new methods to align algorithm decisions when they are found to be non-abiding. The outcomes will include new machine learning theories and frameworks for algorithmic assurance. The significance of the project is that it will offer a crucial platform for certifying algorithms and thus benefit society and businesses in deciding the right Artificial Intelligence algorithms.
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    Active Funded Activity

    Linkage Projects - Grant ID: LP180100750

    Funder
    Australian Research Council
    Funding Amount
    $480,000.00
    Summary
    Personalised Online Learning Analytics by Exploring Multilayer Graph Data . Learning analytics is becoming a significant factor in reducing the drop-out rate of students in online learning. The aim of this project is to develop a reliable, robust, real-time analysis system that automatically reveals multilayered relationships, evaluates students' learning performance, and generates a personal study plan through discovery. This project includes the design of novel algorithms for multilayer graph .... Personalised Online Learning Analytics by Exploring Multilayer Graph Data . Learning analytics is becoming a significant factor in reducing the drop-out rate of students in online learning. The aim of this project is to develop a reliable, robust, real-time analysis system that automatically reveals multilayered relationships, evaluates students' learning performance, and generates a personal study plan through discovery. This project includes the design of novel algorithms for multilayer graph processing, pattern recognition in learning activities, learning performance assessment, and personalised study plan recommendations. The success of this project will significantly enhance the success of online education both in Australia and worldwide and; hence, will save time, money and resources for end users.
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    Funded Activity

    Discovery Projects - Grant ID: DP160103934

    Funder
    Australian Research Council
    Funding Amount
    $410,000.00
    Summary
    Nonparametric Machine Learning for Modern Data Analytics. This project intends to develop next-generation machine-learning methods to cope with the growing data deluge. Modern data analytics tasks need to interpret and derive values from complex, growing data. Intended outcomes of the project include new Bayesian nonparametric methods that can express arbitrary dependency amongst multiple, heterogeneous data sources with infinite model complexity, together with algorithms to perform inference an .... Nonparametric Machine Learning for Modern Data Analytics. This project intends to develop next-generation machine-learning methods to cope with the growing data deluge. Modern data analytics tasks need to interpret and derive values from complex, growing data. Intended outcomes of the project include new Bayesian nonparametric methods that can express arbitrary dependency amongst multiple, heterogeneous data sources with infinite model complexity, together with algorithms to perform inference and deduce knowledge from them; new Bayesian statistical inference for set-valued random variables that moves beyond vectors and matrices to enrich our analytics toolbox to deal with sets; and a new deterministic fast inference to meet with real-world demand.
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    Funded Activity

    Discovery Projects - Grant ID: DP180101051

    Funder
    Australian Research Council
    Funding Amount
    $362,666.00
    Summary
    Smart micro learning with open education resources. This project aims to enhance personalised learning systems for mobile device users . Open online education is gaining in popularity with its ease of use. The project tackles the problems in relation to more and more popular mobile and ‘micro learning’, where people learn on the move and within small units of time. Ontology and machine learning technologies used in this project will help to optimise the offering of open education resources, by p .... Smart micro learning with open education resources. This project aims to enhance personalised learning systems for mobile device users . Open online education is gaining in popularity with its ease of use. The project tackles the problems in relation to more and more popular mobile and ‘micro learning’, where people learn on the move and within small units of time. Ontology and machine learning technologies used in this project will help to optimise the offering of open education resources, by providing solutions meeting each individual learner’s needs. The main outcome will consolidate a cloud based micro learning framework through integrating a group of novel algorithms.
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    Funded Activity

    Linkage Projects - Grant ID: LP100200430

    Funder
    Australian Research Council
    Funding Amount
    $255,000.00
    Summary
    Design of adaptive learning visual sensor networks for crowd modelling in high-density and occluded scenarios. Partnering University of Melbourne researchers, with video surveillance experts SenSen, engineering consultants ARUP and the Melbourne Cricket Club, the project addresses research enabling a system-integrating, existing surveillance, infrastructure to model crowd behaviour and exit strategies, providing real-time analysis, prediction and response capabilities for venue managers and emer .... Design of adaptive learning visual sensor networks for crowd modelling in high-density and occluded scenarios. Partnering University of Melbourne researchers, with video surveillance experts SenSen, engineering consultants ARUP and the Melbourne Cricket Club, the project addresses research enabling a system-integrating, existing surveillance, infrastructure to model crowd behaviour and exit strategies, providing real-time analysis, prediction and response capabilities for venue managers and emergency services. This new capability enhances utilisation of security resources to prevent injury and fatalities in evacuation scenarios, applicable to existing venues and influencing the development of new facilities around the country. The project delivers researcher training, global clientele for local technology and a platform for local industry growth.
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