Google Summer of Code 2017 Projects

Google Summer of Code 2017

We are applying to GSoC 2017. Stay tuned for updates. See the follow up blog posts for how it went in the past.

First step to get involved: Read how to get involved. You will find there why we explicitly encourage female students to apply.

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 (aiming at 3-4 core projects + 1-2 application projects!) - 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.

Joint efforts!

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

Projects

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

• Easy installation
• Unified ML interface, plugin-based architecture
• Shogun Detox II
• Efficient ML: The usual suspects II
• A Kaggle-style pipeline for supervised learning
• Applying Shogun to the real world
• SWIG, Matlab, Julia & modular interfaces

New Algorithms

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.

• Large-Scale Gaussian Processes
• Approximate kernel methods
• Fundamental ML: LGSSMs
• 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

Framework extensions

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.