Reading Group: Machine Learning for Neuroimaging Reading Group

SpeakerDimitrios Athanasakis
DateThursday, 20 Mar 2014
Time16:00 - 17:00
LocationMalet Place Engineering Building 6.12a
Event seriesMachine Learning for Neuroimaging Reading Group

First session on Gaussian Processes for Machine Learning by Rasmussen and Williams:

Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics.The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed.

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