Unified and efficient Machine Learning

The Shogun Machine learning toolbox provides a wide range of unified and efficient Machine Learning (ML) methods. The toolbox seamlessly allows to easily combine multiple data representations, algorithm classes, and general purpose tools. This enables both rapid prototyping of data pipelines and extensibility in terms of new algorithms. We combine modern software architecture in C++ with both efficient low-level computing backends and cutting edge algorithm implementations to solve large-scale Machine Learning problems (yet) on single machines.

We have various ideas for this years Summer of Code. This year, our focus is not on adding new ML algorithms, but on rather software engineering driven goals, in particular

  1. framework re-factoring & clean-ups
  2. efficiency & benchmarks
  3. applications

Please read how to get involved with us before applying. Then please use the scheme shown below for your student application. If you have any questions, ask on the mailing list (shogun-list@shogun-toolbox.org, please note that you have to be subscribed in order to post) or IRC #shogun on freenode.

lightbulb_outline View ideas list


  • c/c++
  • python
  • swig
  • machine learning
  • cmake


  • Science and Medicine
  • machine learning
  • statistics
  • fast algorithms
  • bioinformatics
  • software engineering
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