Search

18 Sep 2023: Chinese History. Classics. Climate Science. Clinical Medicine. Clinical Neuroscience. Computational Biology. ... Computational Neuroscience. Computer Science. Computer Science and Linguistics. Earth Sciences. Economics.

27 Jan 2023: Unsupervised Learning. Variational Approximations. Zoubin Ghahramanizoubin@gatsby.ucl.ac.uk. Gatsby Computational Neuroscience Unit, andMSc in Intelligent Systems, Dept Computer Science.

27 Jan 2023: Unsupervised Learning. Variational Approximations. Zoubin Ghahramanizoubin@gatsby.ucl.ac.uk. Gatsby Computational Neuroscience Unit, andMSc in Intelligent Systems, Dept Computer Science.

27 Jan 2023: Unsupervised Learning. Graphical Models. Zoubin Ghahramanizoubin@gatsby.ucl.ac.uk. Gatsby Computational Neuroscience Unit, andMSc in Intelligent Systems, Dept Computer Science.

27 Jan 2023: Bayesian Monte Carlo. Carl Edward RasmussenandZoubin GhahramaniGatsby Computational Neuroscience Unit. University College London17 Queen Square, London WC1N 3AR, England. ... 102, Novem-ber 1988, Laboratory for Computational Statistics, Department of

2 Oct 2023: I’m using fMRI, EEG, MEG, behavioural measures and single-cell recording data, together with computational modelling to understand these processes better. ... During the PhD, in parallel to the genomics research, I started working with Marieke Mur and

27 Jan 2023: Gatsby Computational Neuroscience Unit. ... PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning, http:///www.pascal-network.org) is a newly launched (December 2003) European Network of Excellence (NoE) as part of its IST program.

27 Jan 2023: Computational Principles of Movement Neuroscience. ... Journal of Neuroscience 16:7085-7096. [Wolpert, D.M., Ghahramani, Z. and Jordan, M.I.

27 Jan 2023: Computational Principles of Movement Neuroscience. ... and Jordan, M.I. (1994). MIT Center for Biological and Computational Learning Technical Report 108, 16 pages.

27 Jan 2023: Unsupervised Learning 2004 Course Web Page. Keywords: Machine learning, probabilistic modelling, graphical models, approximate inference, Bayesian statistics. For a summary of the entire course you can read the following chapter:. Ghahramani (2004)