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Anomaly detection is an important task in data mining. Traditionally most of the anomaly detection algorithms have been designed for ‘static’ datasets, in which all the observations are available
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This project will investigate and develop the ways in which AI algorithms and practices can be made transparent and explainable for use in law enforcement and judicial applications The Faculty
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as networks or graphs in the hope of reasoning about them - but the tools that we have for understanding such network structured data (whether algorithmic analytics or visualisation tools) remain crude
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and communication skills in healthcare. You will use sensor-technology to capture multimodal ‘trace’ data including gestures, speech, workspace spatial layout and manual handling of objects. You will
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particularly interested in supervising students focusing on two broad scenarios: Analytics of the classroom physical space. This would include collecting information via sensors from authentic classrooms and
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Connected Autonomous Vehicle (MCAV) team. Required knowledge Artificial Intelligence Machine learning Software Testing Genetic Algorithms
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headlines around the world when a “work of art created by an algorithm” was sold at auction by Christie’s for $432,500 – nearly 45 times the value estimated before auction. It turned out that the group behind
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on this technology for its automated warehouses and fulfilment centres. The aim of this project is to use discrete optimisation techniques (e.g. Integer Programming) to design new algorithms for MAPF. An example
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Background in Machine Learning, Algorithms and Data Structures
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substantial research project, GPA 80%+ from a reputed university Refereed publications including journal or conference of high repute Desirable Background in Algorithms and Data Structures