[mlpack] GSoC, 16 -implementation of the Bayesian algorithm and decision Tree

[Sushmita singh]

hello,
 As suggested by link
https://github.com/mlpack/mlpack/wiki/SummerOfCodeIdeas I have narrowed
 down to work  on API for dataset and experimentation tool and decision
tree. I am working on my proposal now but do they need to separate?

On Wed, Mar 16, 2016 at 2:33 AM, Sushmita singh <
sushmita.hsingh.apm12 at itbhu.ac.in> wrote:

> hello,
>  As suggested by link
> https://github.com/mlpack/mlpack/wiki/SummerOfCodeIdeas I have narrowed
>  down to work to be  on API for dataset and experimentation tool and
> decision tree. I am working on my proposal now but do they need to
> separate?
>
>
> On Mon, Mar 14, 2016 at 6:14 PM, Ryan Curtin <ryan at ratml.org> wrote:
>
>> On Sun, Mar 13, 2016 at 01:38:00AM +0530, Sushmita singh wrote:
>> > Hello,
>> >
>> >
>> > I am Sushmita Singh, studying in pre-final year of Mathematics and
>> > Computing at Indian Institute of Technology (BHU)-Varanasi, India.
>> Mlpack
>> > is a organization of my interest and I am really looking forward to
>> > contributing to  Mlpack through or beyond GSoC'16. I feel that Mlpack
>> will
>> > provide the advantage to  c++ user for using machine learning so they
>> don't
>> > have diverted to some other language.
>> >
>> > *Relevant Experience:*
>> > I  have a four-year practice of coding in c, c++, java. I have done
>> > projects of ECC (Elliptic Curve Cryptography) at DRDO(Defence Research
>> and
>> > development Organization)  Delhi which is  coded in c++ that has  given
>> me
>> > experience. I am working on my thesis  which is on machine learning so
>> I m
>> > process of learning  about new algorithm.
>> >
>> >
>> > I have built the library in my system  and gone through some of the
>> methods
>> > provided by mlpack. I m biased towards :
>> > *1.conditional decision trees*
>> > *2.Bayesian* *algorithms*.
>> > I want to work on  *Gaussian naive bayers, multinomial naive bayers and
>> > conditional decision trees*.
>> >  Besides going through methods of density elimination trees, naive
>> bayers,
>> > Gaussian Mixture and hidden Markov model, what else should I go through?
>> > please guide me further.
>>
>> Hi Sushmita,
>>
>> Have you taken a look at these pages?
>>
>> http://www.mlpack.org/involved.html
>> https://github.com/mlpack/mlpack/wiki/SummerOfCodeIdeas
>>
>> https://github.com/mlpack/mlpack/wiki/Google-Summer-of-Code-Application-Guide
>>
>> http://write.flossmanuals.net/gsocstudentguide/what-is-google-summer-of-code/
>>
>> Between that and reading the relevant literature for the methods you are
>> interested in, you should be able to prepare a good proposal.
>>
>> Be sure to focus on the API of the algorithms you want to work on in
>> your proposal, so that they match the API of the rest of mlpack.  Maybe
>> this document would also be helpful:
>>
>> https://github.com/mlpack/mlpack/wiki/DesignGuidelines
>>
>> Thanks,
>>
>> Ryan
>>
>> --
>> Ryan Curtin    | "The enemy cannot press a button... if you have
>> ryan at ratml.org | disabled his hand." - Sgt. Zim
>>
>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.cc.gatech.edu/pipermail/mlpack/attachments/20160316/57e4c682/attachment-0002.html>