dc.contributor.author | Bredmar, Fredrik | |
dc.contributor.author | Andersson, Emanuel | |
dc.contributor.author | Bogren, Emil | |
dc.date.accessioned | 2014-09-22T08:11:32Z | |
dc.date.available | 2014-09-22T08:11:32Z | |
dc.date.issued | 2014-09-22 | |
dc.identifier.uri | http://hdl.handle.net/2077/36987 | |
dc.description.abstract | We 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.title | Scalable Machine Learning for Big Data | sv |
dc.type | text | |
dc.setspec.uppsok | Technology | |
dc.type.uppsok | M2 | |
dc.contributor.department | Göteborgs universitet/Institutionen för data- och informationsteknik | swe |
dc.contributor.department | University of Gothenburg/Department of Computer Science and Engineering | eng |
dc.type.degree | Student essay | |