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Adversarial Machine Learning (AML) is a technique to fool a machine learning model through malicious input. Due to its significance in many scenarios, including security, privacy, and health
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, sociological, economic and web data this project will apply machine learning to build a recommendation engine that can provide life advice and predictions to individuals based on their current life situation and
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With success stories ranging from speech recognition to self-driving cars, machine learning (ML) has been one of the most impactful areas of computer science. ML’s versatility stems from the wealth
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based on matched-filter statistics. Detecting the unknown relies on the development of complex algorithms at the forefront of statistics, machine learning, and data science. This multi-disciplinary
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ecosystem interactions. If used wisely for decision-support, these technologies can help select and implement effective policies. This PhD project, jointly offered by Monash University (Australia) and
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Project description: Nowadays, data-driven machine learning algorithms are well suited to solve real-world problems that require high-level prediction accuracy. However, it seems as if nothing beats
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The world is dynamic and in a constant state of flux, yet most machine learning models learn static models from a dataset that represents a single snapshot in time. My group's research is
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On-device machine learning (ML) is rapidly gaining popularity on mobile devices. Mobile developers can use on-device ML to enable ML features at users’ mobile devices, such as face recognition
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Machine learning has recently made significant progress for medical imaging applications including image segmentation, enhancement, and reconstruction. Funded as an Australian Research Council
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FIT Indigenous Industry Based Learning Scholarship Sir John Monash Scholarship for Excellence Indigenous students enrolling in an undergraduate Information Technology degree at Monash University