Scalable Machine Learning for Big Data

Detta är en Kandidat-uppsats från Göteborgs universitet/Institutionen för data- och informationsteknik

Författare: Fredrik Bredmar; Emanuel Andersson; Emil Bogren; [2014-09-22]

Nyckelord: ;

Sammanfattning: We describe each step along the way to create a scalable machine learning system suitableto process large quantities of data. The techniques described in the report will aidin creating value from a dataset in a scalable fashion while still being accessible tonon-specialized computer scientists and computer enthusiasts. Common challenges inthe task will be explored and discussed with varying depth. A few areas in machinelearning will get particular focus and will be demonstrated with a supplied case-studyusing weather data courtesy of the Swedish Meteorological and HydrologicalInstitute.

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