[mlpack] GSOC-2020 | Project Proposal | Application Based Machine Learning Course using MLPack

[Marcus Edel]

Hello Shivam,

thanks for reaching out, I generally like the idea, however, I'm not sure it's
something we can mentor during GSoC, Google generally expects students to code,
there was Season of Docs last year, which might fit better. You said RedHen Labs
did a similar project, do you have a link to the project?

Thanks,
Marcus

> On 19. Mar 2020, at 16:58, Shivam Behl <shivambehl123 at gmail.com> wrote:
> 
> Hello Mentors,
> 
> I am Shivam Behl, 3rd year undergraduate at Thapar University, India.
> I want to propose an "application-based Machine Learning course for MLPack" for GSOC 2020. I have seen a previous GSOC Project which dealt with making machine learning course for Humanity students under the organization - RedHen Labs. Please review this short proposal and tell if it is GSOC level.
> 
> I have spent last week learning and understanding MLPack library. During this period I realized that while learning MLPack, one is quite on your its for a lot of time. Documentation is good, but many parts are not clearly explained and one has to refer the source code to understand a lot of stuff. This is is not a big deal for a seasoned programmer fluent in C++, but can be a cause of trouble for new entrants.
> 
> I believe having a machine learning course based on MLPack can help boost the user base of the library and help find applications in new niches.
> 
> I propose to make the course around these points - 
> 1. Basic Machine Learning Implementations using C++ and Python Wrapper.
> 2. Practical applications using Mini Projects.
> 3. Understanding CodeBase
> 
> Work will be in three phases, as follows - 
> -- Phase 1 --
> The intuition of Machine Learning Algorithm.
> Application using ML Pack.
> This pattern will be followed for all the Algorithms implemented in MLPack.
> 
> -- Phase 2 --
> Hands-on Tutorials on Implementing MLPack on free to use Datasets.
> Interesting visualizations and comparisons with other libraries.
> 
> -- Phase 3 --
> Silent C++ features used in MLPack codebase.
> Basics of how the code is implemented.
> Appendix of Armadillo, Numpy Stack, etc.
> 
> The Course will involve slides, pdf for detail text and code snippets.
> 
> Presently, I am thinking of the following hands-on Tutorials to be a part of the course - 
> --- Stock Market analysis and building algo-trading model using basic ML Algorithms.
> --- Style transfer using ANN
> will add more soon. 
> 
> Please let me know if this project is suitable for GSOC and what changes I should make to the overall plan.
> 
> Regards,
> Shivam Behl
> _______________________________________________
> mlpack mailing list
> mlpack at lists.mlpack.org
> http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack