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.
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