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dc.contributor.authorBredmar, Fredrik
dc.contributor.authorAndersson, Emanuel
dc.contributor.authorBogren, Emil
dc.date.accessioned2014-09-22T08:11:32Z
dc.date.available2014-09-22T08:11:32Z
dc.date.issued2014-09-22
dc.identifier.urihttp://hdl.handle.net/2077/36987
dc.description.abstractWe describe each step along the way to create a scalable machine learning system suitable to process large quantities of data. The techniques described in the report will aid in creating value from a dataset in a scalable fashion while still being accessible to non-specialized computer scientists and computer enthusiasts. Common challenges in the task will be explored and discussed with varying depth. A few areas in machine learning will get particular focus and will be demonstrated with a supplied case-study using weather data courtesy of the Swedish Meteorological and Hydrological Institute.sv
dc.titleScalable Machine Learning for Big Datasv
dc.typetext
dc.setspec.uppsokTechnology
dc.type.uppsokM2
dc.contributor.departmentGöteborgs universitet/Institutionen för data- och informationsteknikswe
dc.contributor.departmentUniversity of Gothenburg/Department of Computer Science and Engineeringeng
dc.type.degreeStudent essay


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