Hello Luca, A good first step is to become very familiar with mlpack's abstractions and implementation. For the particle swarm project, you can look at the Optimizer API https://arxiv.org/abs/1711.06581 or at the existing code: https://github.com/mlpack/mlpack/tree/master/src/mlpack/core/optimizers; that might be a good place to start.
For the reinforcement learning and profiling project, there is a fair amount of discussion about both from previous years, as they are recurring projects. Here is an example: http://knife.lugatgt.org/pipermail/mlpack/2017-March/thread.html The String Processing project is not polished yet and is in need of updating, unfortunately, we haven't had a chance to get around to it yet. So, unfortunately, you'll have to spelunk for further documentation on how the project can work. Also in case, you haven't seen it: mlpack.org/gsoc.html and www.mlpack.org/involved.html might be helpful. I hope this was helpful, don't hesitate to ask if we should clarify anything. Thanks, Marcus > On 13. Jan 2018, at 15:04, Luca Foschiani <[email protected]> wrote: > > Hello, > I'm a computer science student at the University of Udine (Italy). > I got my bachelor's degree (in computer science) in 2016 and I'm currently > working towards my master's degree. My studies are focused on optimization > algorithms and artificial intelligence. > > Some time ago I heard about Google Summer of Code and I began reading about > it. > I thought it would be a really interesting opportunity, so I started learning > more about the organizations which were involved in the 2017 GSoC and mlpack > is one of them. I also briefly read the descriptions of the 2017 projects > which were done by past students. > > The main reasons why I'm interested in mlpack are that C++ is the language > which I used the most during the past few years (both for academic purposes > and for personal projects), and artificial intelligence (machine learning in > particular), together with optimization are the main topics of my master's > degree. > > I have already read the ideas for GSoC 2018, and they seem interesting to me. > In particular, particle swarm optimization, reinforcement learning, string > processing, profiling caught my attention, but I would be interested in > pretty much any other idea as well (among the ones listed in the page). > > I just wanted to ask if you could point me in the right direction in order to > start thinking about what I could do for mlpack. > > Thank you, > Luca Foschiani > _______________________________________________ > mlpack mailing list > [email protected] > http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack
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