[mlpack] looking for guidance and opinions

[Anuraj Kanodia]

Hello,
I am Anuraj, a Computer Science(B.E.) and Mathematics(M.Sc.) student.
I have been going through the mlpack codebase since quite some time now
(since this September to be precise). I am already familiar with the mlpack
workflow as i have made a few contributions to mlpack.

/* a little background */
I am approaching the final stages of my graduation (currently in pre-final
year), so i have started looking for topics for my thesis. After exploring
a lot of options, i finally see my search concluding with ML.
I went through the ideas page on the mlpack wiki and also went through some
research papers to get a brief overview of the different aspects of ML.
In particular, i went through some chapters of Ryan Curtin's dissertation
(Improving Dual Tree Algorithms). I will be using it as part of my
study-oriented project in the next semester. This project will help me
further refine my thesis topic.
In the meantime, I also implemented a few ML classifiers such as Decision
Trees(using ID3 algorithm), Neural Networks(backpropagation algorithm) for
face/pose/sunglass recognition, and the Naive Bayes Algorithm (for face
recognition). The code for the above can be found in my github profile (
[1] ). Do note that the focus was on implementation rather than on writing
a highly optimized code.
Out of these three, ANNs really caught my attention. The power of Neural
Networks amazes me and i see this as another potential topic for my thesis
(especially after coming across Google's 'Quick, Draw!' ( [2] )).

/* the point */
So, right now i am interested in these two topics:

   1. Dual Tree Algorithms.
   2. Neural Networks.

*I would be grateful to you if you could recommend some relevant sources
which would further shed some light on these topics. I would also like to
hear your opinion on these topics. *

I already plan on completing this course on neural networks over the
winter: https://www.coursera.org/learn/neural-networks

Also, the 'Essential Deep Learning Modules' project from the ideas page is
relevant here (from what i understand, it wasn't taken up by anyone). I
think this project will give me a chance to learn about different
fundamental networks. I am yet to take a look at the relevant tickets and
references mentioned there though. *Are there any other sources i should
refer in order to prepare for the project? *

I look forward to hearing from you.

Thank you!

[1] https://github.com/akanuraj200/MachineLearning

[2] https://quickdraw.withgoogle.com/#


<https://github.com/akanuraj200/MachineLearning>
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