Google Summer of Code 2017 Projects

Google Summer of Code 2017

We are applying to GSoC 2017. Stay tuned for updates. This page is work in progress, based on a copy-paste from last year.

See the follow up blog posts for how it went.

Main focus

This year's GSoC is about improving and showcasing Shogun, rather than extending it (exceptions allowed). We mainly want to recruit new long-term developers.

• Focus on existing algorithms: We want to improve our algorithms - easier use, efficiency, better documentation and more applications - rather than just adding more algorithms.
• Focus on students: As in 2016, we aim to have fewer students - more intense mentoring, interaction between students, blogging and documenting for individual students.
• Focus on application: This year, we would like to use Shogun to solve some real life ML problems in a self-contained project. If you have a cool idea, let us know.

Projects

In addition to the technical project, all students will:

• add to our example/testing system: http://shogun.ml/examples
• peer-review a fellow student's work
• jointly help our ever growing issue list and work on a release
• contribute to our GSoC blog

Continue the Plugin Architecture effort

Last year we've started to work on a new parameter framework that additional will help us to break shogun into smaller plugins, see the last year's project [here](GSoC 2016 project New Parameter Framework and Plugin Architecture).

This year we would like to continue this effort.

(outdated) - project ideas

Project Ideas below are roughly ordered by priority and projects in bold type are more likely to happen.

Improving Shogun's infrastructure

Projects improving Shogun are the main focus of this year's GSoC. They are roughly ordered by priority and most of them do not focus on Machine Learning but rather on software engineering.

• Unified ML interface, plugin-based architecture
• A Shogun Detox
• SWIG, Matlab & modular interfaces
• Native MS Windows port

Extending Shogun

Note that projects extending Shogun have a lower priority than projects improving Shogun. If algorithms related projects will happen, they are likely to be based around improvements rather than adding new ones.

Algorithms

• Fundamental ML: The usual suspects
• Large-Scale Gaussian Processes
• Hip Deep learning
• Approximate kernel methods
• Fundamental ML: LGSSMs
• Density Estimation in Infinite Dimensional Exponential Families
• Large scale statistical testing
• HMM cleanup and application
• Solver for the KKT System
• Dual coordinate ascent solver for SO-SVM
• LP/QP Framework
• Debiasing & Cluster computing

Framework

• MCMC & Stan
• Unifying Shogun's linear algebra
• Flexible modelselection 2
• Independent jobs Framework
• Shogun cloud extensions

Other ideas:

We are also open for your ideas: If you have a cool idea for an application or collaboration with another project, let us know! To add your project, please create a new wiki page for each project that you describe. Name them as "GSoC_2016_project_XXX" etc. Here is a template.

• Cool pipelines:
  • A kaggle pipeline for supervised prediction.
  • Spectrometer (there is an open-source hardware project on this)
  • Music brainz predictions (The cool hair guy at GSoC is the one we should talk to here)
  • Some biology thing?
  • Collaboration with MLPack for toolkit wide performance/accuracy testing. See their GSoC 2013 project

Infrastructure:

• REST interface

Our list of projects is a growing list.