[mlpack] Google Summer of Code 2013 - Introduction + Thoughts

[Marcus Edel]

Hello,

I'm interested in working for mlpack this summer in a GSoC project and would be happy to work on the automatic benchmarking of the mlpack methods.

I would like to start by introducing myself. I'm a undergraduate student in computer science at Free University of Berlin (Freie Unversität Berlin, Germany). 
I'm a highly motivated student with interests in many fields of artificial intelligence, especially machine learning. I plan to study artificial intelligence after I complete my bachelor's degree. 

I have taken courses in machine learning and have basic knowledge of machine learning algorithms like Bayesian classification, regression, AdaBoost, support vector machines, neural networks, etc. and looking for an opportunity to learn more about machine learning, image processing and pattern recognition.

I'm proficient in c/c++, java, matlab and python. Unfortunately i have no experience with the jenkins software but I’m confident I can still manage to write code for it.

I've looked at the code and noticed that a lot of the mlpack methods have a test which is supposed to be run with some set of parameters, but a lot of them generate random datasets. With regard to informing the developers which of their changesets have caused speedups or slowdowns particularly with regard to compare the results with the competing libraries, I consider it advisable to take existing datasets from something like mldata.org (mldata.org provides data and task downloads in a standardized format), so it would be good to expand the task to add read support for the mldata.org datasets.

Currently I'm looking into the coding practices used in mlpack and play with some features of mlpack. I would like to request the mentors and the community to please provide any details to resources which could be helpful for the project.

Best regards.

Marcus