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Google Summer of Code 2016 Projects
Google Summer of Code 2016
Thanks everyone for a great summer. See the follow up blog posts for how it went.
This year's GSoC is about improving Shogun, rather than extending it (exceptions allowed). We also 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: We want to have fewer students - more intense mentoring, interaction between students, blogging and documenting for individual students.
In addition to the technical project, all students will:
- work on a joint GSoC project: a Shogun cookbook
- peer-review a fellow student's work
- jointly help with the 5.0 release
- contribute to our GSoC blog
Easy Installation on Major Platforms
[link to project page](GSoC 2016 project Easy Installation on Major Platforms)
[link to project page](GSoC 2016 project Shogun Detox)
New Parameter Framework and Plugin Architecture
[link to project page](GSoC 2016 project New Parameter Framework and Plugin Architecture)
Fundamental ML: the usual suspects
[link to project page](GSoC 2016 project Fundamental ML the usual suspects)
(outdated) - project ideas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.
- Easy installation on major platforms (MOST IMPORTANT)
- Unified ML interface, plugin-based architecture
- A Shogun Detox
- SWIG, Matlab & modular interfaces
- Native MS Windows port
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.
- 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
- MCMC & Stan
- Unifying Shogun's linear algebra
- Flexible modelselection 2
- Independent jobs Framework
- Shogun cloud extensions
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
Our list of projects is a growing list.