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This talk gives an overview of machine learning from a practical perspective. Starting with examples of problems we might want to solve (in vision, signal processing, and geospatial inference), and the assumptions we have to make in order to get anywhere, it then covers a number of different supervised and unsupervised learning techniques. The talk concludes with ideas on how to evaluate a system, and when we should believe that a model is "right".
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Video Length: 3451
Date Found: April 01, 2011
Date Produced: March 31, 2011
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The explosion in growth of the Web of Linked Data has provided, for the first time, a plethora of information in disparate locations, yet bound together by machine-readable, semantically typed relations. Utilisation of the Web of Data has been, until now, restricted to members of the community, ...
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