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27 Jan 2023: Why is this useful? Machine Learning Machine learning is an interdisciplinary field focusing on both the mathematical foundations and practical applications of systems that learn, reason and act. • ... How does it fit into Information Engineering? •

Andrew Wilson is in his second year of a PhD in machine learning, in the Computational and Biological Learning Group. ... Machine learning is partly inspired by advances in neuroscience, and is focused on developing algorithms for learning and decision

15 Oct 2019: Research interests . 1. Probabilistic machine learning. 2. Computational neuroscience. In my doctoral research I will investigate how we can learn useful probabilistic representations from data in a fully unsupervised manner, given ... machine learning

28 Jan 2022: Information Engineering at Cambridge spans the broad areas of control, communications, signal, speech, image and vision processing, machine learning, and computational neuroscience. ... the cellular basis of learning and memory, control of neuronal

4M22. Climate Change Mitigation. 4M23. Electricity and Environment (TPE22). 4M24. Computational Statistics and Machine Learning. ... 4G3. Computational Neuroscience. 4G5. Materials and Molecules: Modelling, Simulation and Machine Learning.

describe models of plasticity and learning and how they apply to the basic paradigms of machine learning (supervised, unsupervised, reinforcement) as well as pattern formation in the nervous system. ... Content. The course covers basic topics in

describe models of plasticity and learning and how they apply to the basic paradigms of machine learning (supervised, unsupervised, reinforcement) as well as pattern formation in the nervous system. ... Content. The course covers basic topics in

describe models of plasticity and learning and how they apply to the basic paradigms of machine learning (supervised, unsupervised, reinforcement) as well as pattern formation in the nervous system. ... Content. The course covers basic topics in

describe models of plasticity and learning and how they apply to the basic paradigms of machine learning (supervised, unsupervised, reinforcement) as well as pattern formation in the nervous system. ... Content. The course covers basic topics in

describe models of plasticity and learning and how they apply to the basic paradigms of machine learning (supervised, unsupervised, reinforcement) as well as pattern formation in the nervous system. ... Content. The course covers basic topics in