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The Generalised Gaussian Process Convolution Model 10.17863/CAM.20389 Bruinsma, Wessel Turner, Richard This thesis formulates the Generalised Gaussian Process Convolution Model (GGPCM), which is a generalisation of the Gaussian Process Convolution Model presented by Tobar et al. [2015b]. The GGPCM provides a theoretical framework for nonparametric kernel models of multidimensional signals defined on multidimensional input spaces. We show that the GGPCM generalises and connects existing work; most notably, we derive a dual formulation of the cross-spectral mixture kernel presented by Ulrich et al. [2015]. Finally, we use the GGPCM to develop the Deep Kernel Model, which presents a new network structure for unsupervised learning. University of Cambridge 2016-10-22 Thesis Masters Master of Philosophy (MPhil) en https://www.repository.cam.ac.uk/handle/1810/273357 https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/800f5236-a9c6-4c71-8053-7eeb35eab557/download 87eda9de84448d1f82354d60eee3eb5f https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/5297f2e6-e1ab-460d-a9c5-efa15fc4fc53/download 085a633a8b811198c323eace1c1eda4b https://www.rioxx.net/licenses/all-rights-reserved/ machine learning Gaussian process kernel nonparametric kernel multi-task learning