GSoC 2014

We are applying as mentoring organization for Google Summer of Code 2014

Stay tuned in melange for news regarding the accepted organizations (announcement on February 24th). Furthermore, you are very welcome to check our ideas list for GSoC 2014 and see if you are interested in any of the projects. Of course, please do not hesitate to contact us suggesting your own project idea. We really appreciate these initiatives!


CfP: Google Summer of Code (Deadline 21 March, 19hrs UTC)

Call for Participation

We are looking for interested students to join us in improving the shogun machine learning toolbox in this year's google summer of code.

Timeline

Application deadline is March 21, 19hrs UTC and the program will run from May to the middle of August (cf. http://www.google-melange.com/gsoc/events/google/gsoc2014).

About Google Summer of Code

Google Summer of Code is a global program that offers students stipends ($5500 / per student) to write code for open source projects.

About the shogun machine learning toolbox

SHOGUN is a machine learning toolbox, which is designed for unified large-scale learning for a broad range of feature types and learning settings. It offers a considerable number of machine learning models such as support vector machines for classification and regression, hidden Markov models, multiple kernel learning, linear discriminant analysis, linear programming machines, and perceptrons. Most of the specific algorithms are able to deal with several different data classes, including dense and sparse vectors and sequences using floating point or discrete data types. We have used this toolbox in several applications from computational biology, some of them coming with no less than 10 million training examples and others with 7 billion test examples. With more than a thousand installations worldwide, SHOGUN is already widely adopted in the machine learning community and beyond.

SHOGUN is implemented in C++ and interfaces to all important languages like MATLAB, R, Octave, Python, Lua, Java, C#, Ruby and has a stand-alone command line interface. The source code is freely available under the GNU General Public License, Version 3 at http://www.shogun-toolbox.org.

During Summer of Code 2013 we intend to improve the accessibility of the library. Additionally we will be complementing it with promising (new) machine learning algorithms.

How to Apply

  • To apply for shogun, select one or multiple topics from our ideas list or propose topics you are highly interested in - we might be very interested too :)
  • See the document we prepared for you
  • Before applying, please ensure that you have your working environment set up, i.e. checkout shogun from git and successfully compiled its relevant parts (see instructions onhttp://www.shogun-toolbox.org) and indicate that you have done so. In addition, it is required to contribute at least a small patch to get you set up (e.g. by attacking one of the entry tasks) and to be considered.
  • Please note that we have an application template that following will incredibly help us to process your application. It is at the bottom of the website (link follows below). This webpage also has the register for GSoC application link.

If you have further questions don't hesitate to ask on the shogun mailinglist (shogun-list@shogun-toolbox.org, please note that you have to be subscribed in order to post) or on irc.freenode.net channel #shogun.

Additional Resources

  • In case you are unsure if you are good enough or have other questions check out the student FAQ.
  • Some general guidelines how to make a good impression :)


We are Participating in the Google Summer of Code 2014 Program

In case you are a talented student interested in a summer project, we are looking for you!

Check out our ideas list and instructions on how to apply. To get an idea what shogun is about check out the documentation and read our overview paper:

Soeren Sonnenburg, Gunnar Raetsch, Sebastian Henschel, Christian Widmer, Jonas Behr, Alexander Zien, Fabio de Bona, Alexander Binder, Christian Gehl, and Vojtech Franc. The SHOGUN Machine Learning Toolbox . Journal of Machine Learning Research, 11:1799-1802, June 2010.


What's New

Feb. 17, 2014 -> SHOGUN 3.2.0
Jan. 6, 2014 -> SHOGUN 3.1.1
Jan. 5, 2014 -> SHOGUN 3.1.0
Oct. 28, 2013 -> SHOGUN 3.0.0
March 17, 2013 -> SHOGUN 2.1.0
Sept. 1, 2012 -> SHOGUN 2.0.0
Dec. 1, 2011 -> SHOGUN 1.1.0