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Engineering Tripos Part IIB, 4G3: Computational Neuroscience, 2022-23 …
https://teaching.eng.cam.ac.uk/content/engineering-tripos-part-iib-4g3-computational-neuroscience-2022-23describe 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 -
Engineering Tripos Part IIB, 4G3: Computational Neuroscience, 2020-21 …
https://teaching.eng.cam.ac.uk/content/engineering-tripos-part-iib-4g3-computational-neuroscience-2020-21describe 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 -
Engineering Tripos Part IIB, 4G3: Computational Neuroscience, 2019-20 …
https://teaching.eng.cam.ac.uk/content/engineering-tripos-part-iib-4g3-computational-neuroscience-2019-20describe 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 -
Engineering Tripos Part IIB, 4G3: Computational Neuroscience, 2018-19 …
https://teaching.eng.cam.ac.uk/content/engineering-tripos-part-iib-4g3-computational-neuroscience-2018-19describe 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 -
Engineering Tripos Part IIB, 4G3: Computational Neuroscience, 2017-18 …
https://teaching.eng.cam.ac.uk/content/engineering-tripos-part-iib-4g3-computational-neuroscience-2017-18describe 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 -
Andrew Wilson wins Best Student Paper Award at the Uncertainty in…
https://www.eng.cam.ac.uk/news/andrew-wilson-wins-best-student-paper-award-uncertainty-artificial-intelligence-conferenceAndrew 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 -
Edoardo Chidichimo | Department of Psychology
https://www.psychol.cam.ac.uk/staff/edoardo-chidichimo18 Jul 2024: In the future, Edoardo hopes to combine computational approaches (e.g., machine learning and social network analyses) to anthropologically- and ethnographically-derived hypotheses in order to better predict and prevent violent ... Interdisciplinarity; -
paper8-lect0-13
https://mlg.eng.cam.ac.uk/zoubin/p8-07/lect0.pdf27 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? • -
Jonathan So - 2019 Cohort | Harding Distinguished Postgraduate…
https://www.hardingscholars.fund.cam.ac.uk/jonathan-so-2019-cohort15 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 -
Engineering Tripos, Part IIB: Notice concerning Engineering Areas |…
https://teaching.eng.cam.ac.uk/content/engineering-tripos-part-iib-notice-concerning-engineering-areas4M22. 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.
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