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. Application of artificial intelligence/machine learning to the big data from genetics and omics is well recognized in healthcare, however, its application to the data reported everyday as part of the clinical
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in diverse, real-world environments. Both classical machine learning methods and deep learning techniques can be employed to tackle this task. This project aims to achieve several objectives: 1
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of Machine Learning (ML) models across large-scale distributed systems. Leveraging advanced AI and distributed computing strategies, this project focuses on deploying ML models on real-world distributed
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Monash Residential Services - Front Office, Admissions and Operations. The Program ensures that the graduates will gain experience through on-the-job learning and develop the ability to provide a range of
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hands-on technician with a strong technical foundation and the drive to learn. We’re looking for: Background in electrical, electronics, instrumentation, automotive, auto electrician or cabling trades
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and Boulton, 1968; Wallace and Dowe, 1999a; Wallace, 2005) is a Bayesian information-theoretic principle in machine learning, statistics and data science. MML can be thought of in different ways - it
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This PhD project is part of a larger project that aims to explain the uncertainty of Machine Learning (ML) predictions. To this effect, we must quantify uncertainty, devise algorithms that explain
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networks that can be trained to do machine learning and AI tasks in a similar way to artificial neural networks. In this project you will develop machine learning theory that is consistent with the learning
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. Required knowledge Python programming Machine learning background Image analysis Video analysis Audio analysis
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of Machine Learning as the problem of approximating function f from the pair of measurements (x,y), and Optimization as the problem of finding the value of input x that maximizes the output y given