This repository serves the purpose of being a one-stop shop for all the materials related to the MachineLearningClub group at the IMBEI.
Nov 2020 - MMM 2021, we will read Deep Learning with R
Mar 2020 - Jul 2020, we have read Introduction to Statistical Learning.
See the schedule wiki for the schedule of presentations.
This repository represents a coordinated effort of the IMBEI research fellows, spearheaded by the Biostatistics Division.
During active semesters we hold weekly meetings, where a chapter of a book is presented by [] with a focus on modern applied statistical methodology and using the R language.
Our meetings are open to all (see details below), and materials we produce are licensed under the Creative Commons Attribution-ShareAlike 4.0 International Public License.
Honoring the license itself of the very source of inspiration for the structure of this repo, we acknowledge the repo from the Waldron Lab https://github.com/waldronlab/data-science-seminar.
We hope you find these materials useful and will join our sessions.
Leave a comment on the "Welcome" Issue to introduce yourself and to let us know your GitHub username.
Install R and RStudio (latest stable versions are very fine for this) - highly recommended for adopting the framework to create, edit, and further develop the reading/presentation materials.
Install RMarkdown and knitr, and get familiar with them - no pro knowledge is needed, although the more the merrier; plus, of course, these tools are potentially a huge productivity boost in many of your activities, so they are highly recommended
Install git and get familiar with GitHub. Sign up for a GitHub account, then introduce yourself on the "Welcome" issue of this repository, under Issues. You will then be able to contribute your presentation and/or exercise notes using file upload directly here, or by using git. If you want to use git instead of simple file upload but don't know what that means, follow this tutorial. The process in RStudio is documented here or there is a video here.
Pick the date or topic that best suits you and reserve it on the presentation schedule wiki, adding your name/GitHub username to the schedule table.
Read the required section of the book, and do the associated exercises that you will present.
Edit the presentation file, using the template provided, and place it in the folder corresponding to the textbook name (and respective chapter). See more information about making Xaringan presentations.
Commit the presentation to GitHub so that it is available to others. Don't know what that means? The process is documented here or there is a video here.
Past textbooks have included:
Future textbooks will include...
Do you have a book to suggest? Please do so by posting to the specific issue (check before if someone else did suggest this). Make sure to post some reference to your suggestion, to make it easy to assess its contents.