mauriciogtec / sds385

SDS385: Stats Models for Big Data

About this repository...

This my personal page for SDS 385. Here I will share my solutions to exercises as well as discussions that I find important. I am super happy to receive feedback, and even better, pull requests!.

Cheers

By the way, click here if you didn't get the joke... Who is Big Data?

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My solutions are in html, created with RMarkdown (even if there's no R formulas, RMarkdown comes preconfigured with all the necessary MathJax and CSS stuff necessary, so it's cool...).

Unfortuntaley, Github is not as friendly with Rmd as it is with Jupyter notebooks, say..., so I created a Github Page at mauriciogtec.github.io/SDS385, so that you can see the pretty html version. Below are also direct links:

• Solution01a
• Solution01b
• Solution02
• Solution04:
  • readsvm The R/Rcpp package I created for reading SVM light data. You can install this package on any machine using the command devtools::install_github("SDS385team/readsvm").
  • sparselogit The R/Rcpp package I created to perform logistic regression on big sparse data set with lazy updating and stochastic gradient descent. You can install this package on any machine using the command devtools::install_github("SDS385team/sparselogit")
  • vignette A brief report of the results using sparselogit on the SPAM dataset.
• Solution05a
• Solution05b
• Solution06