Google Summer of Code 2017 Projects
We applied were accepted to GSoC 2017. Here you can find an overview of our projects and follow-up blog posts .
This page was our ideas page.
First step to get involved: Read how to get involved, and that the best way is to solve entrance tasks. In addition to your "technical application", we have collected some tips for your written application. We explicitly encourage female students to apply.
Please do not contact Shogun developers directly, but always use the mailing list, GitHub, or StackOverflow.
See here for the list of participating mentors and students.
As in 2016, 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.
This year, we would like to
- Focus on improving the existing: We want to improve our algorithms - easier use, efficiency, better documentation and more applications - rather than just adding more algorithms. We want to improve Shogun's core framework and the installation process.
- Focus on students: We aim to have fewer students (aiming at 3-4 core projects + 1-2 application projects!) - more intense mentoring, interaction between students, blogging and documenting for individual students.
- Focus on applications: 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.
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
- The low hanging fruit project
Project Ideas below are roughly ordered by priority and projects in bold type are more likely to happen.
- Shogun Detox II
- Efficient ML: The usual suspects II
- A Kaggle-style pipeline for supervised learning
- Applying Shogun to the real world
- SWIG interfaces: Matlab, Javascript, clean-ups
- Amalgamation
Note that projects extending Shogun have a lower priority than projects improving Shogun. If you are really keen and can show that you are the right person, we might consider (max) one project here.
- Fundamental ML: LGSSMs
- Approximate kernel methods
- Large-Scale Gaussian Processes
- Hip Deep learning
- 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
Again, If you are really keen and can show that you are the right person, we might consider (max) one project here.
- MCMC & Stan
- Flexible modelselection 2
- Independent jobs Framework
- Shogun cloud extensions
- REST interface
Our list of projects is a growing list.