R4DS Introduction to Statistical Learning Using R Book Club

Welcome to the R4DS Introduction to Statistical Learning Using R Book Club!

We are working together to read Introduction to Statistical Learning Using R by Gareth James, Daniela Witten, Trevor Hastie, and Rob Tibshirani (Springer Science+Business Media, LLC, part of Springer Nature, copyright 2021, 978-1-0716-1418-1_1). Join the #book_club-islr channel on the R4DS Slack to participate. As we read, we are producing notes about the book.

Meeting Schedule

If you would like to present, please add your name next to a chapter using the GitHub Web Editor!

Cohort 1: (started 2021-09-21) - Tuesdays, 10:00am EST/EDT

Past Meetings

• 2021-09-21: Chapter 1: Introduction - Jon Harmon
• 2021-09-28: Chapter 2: Statistical Learning (part 1) - Ray Balise
• 2021-10-05: Chapter 2: Statistical Learning (part 2) - Ray Balise and Jon Harmon
• 2021-10-12: Chapter 3: Linear Regression (part 1) - Jon Harmon
• 2021-10-19: Chapter 3: Linear Regression (part 2) - August
• 2021-10-26: Chapter 3: Linear Regression (lab) - Jon Harmon
• 2021-11-02: NO MEETING (Fallback Break)
• 2021-11-09: Chapter 4: Classification (part 1) - Mei Ling
• 2021-11-16: Chapter 4: Classification (lab) - Ray Balise
• 2021-11-23: Chapter 4: Classification (part 2) - Kim Martin
• 2021-11-30: Chapter 5: Resampling Methods (part 1) - Laura Rose
• 2021-12-07: Chapter 5: Resampling Methods (part 2) - Justin Dollman
• 2021-12-14: Chapter 6: Linear Model Selection and Regularization (part 1) - Justin Dollman
• 2021-12-28 & 2022-01-04: NO MEETINGS (Winter Break)
• 2022-01-11: Chapter 6: Linear Model Selection and Regularization (part 2) - Justin Dollman
• 2022-01-18: Chapter 6: Linear Model Selection and Regularization (Lab) - Federica Gazzelloni
• 2022-01-25: Chapter 7: Moving Beyond Linearity (part 1) - Justin Dollman
• 2022-02-01: Chapter 7: Moving Beyond Linearity (part 2) - Justin Dollman
• 2022-02-08: Chapter 8: Tree-Based Methods (part 1) - Justin Dollman
• 2022-02-15: Chapter 8: Tree-Based Methods (part 2) - Justin Dollman
• 2022-02-22: Chapter 8: Tree-Based Methods (lab) - Laura Rose
• 2022-03-01: Chapter 9: Support Vector Machines - Laura Rose

• 2022-03-08: Chapter 9: Support Vector Machines (lab) - Jon Harmon
• 2022-03-15: Chapter 10: Deep Learning (part 1) - Federica Gazzelloni
• 2022-03-22: Chapter 10: Deep Learning (lab) - TBD
• 2022-03-29: Chapter 11: Survival Analysis and Censored Data (part 1) - Justin Dollman
• 2022-04-05: Chapter 11: Survival Analysis and Censored Data (lab) - TBD
• 2022-04-12: Chapter 12: Unsupervised Learning (part 1) - Jon Harmon
• 2022-04-19: Chapter 12: Unsupervised Learning (lab) - Jon Harmon
• 2022-04-26: Chapter 13: Multiple Testing (part 1) - Mei Ling Soh
• 2022-05-03: Chapter 13: Multiple Testing (lab) - Mei Ling Soh

Cohort 2: (starts 2021-12-02) - Tuesdays, 10:00am CST

Past Meetings

• 2021-12-02 Chapter 1: Introduction - Federica Gazzelloni
• 2021-12-09 Chapter 2: Statistical Learning - Jim Gruman
• 2021-12-16 Chapter 2: Statistical Learning (Lab) - Jim Gruman
• 2021-12-23 NO MEETING
• 2021-12-30 NO MEETING
• 2022-01-06 Chapter 3: Linear Regression - Ricardo J. Serrano
• 2022-01-13 Chapter 3: Linear Regression (Lab) - Ricardo J. Serrano
• 2022-01-20 Chapter 4: Classification - Michael Haugen
• 2022-01-27 Chapter 4: Classification (Lab) - Michael Haugen
• 2022-02-03 Chapter 5: Resampling Methods - (Lab) Ricardo J. Serrano & Federica Gazzelloni
• 2022-02-10 Chapter 5: Resampling Methods - Team
• 2022-02-17 Chapter 6: Linear Model Selection and Regularization - Federica Gazzelloni
• 2022-02-24 Chapter 6: Linear Model Selection and Regularization (Lab) - Federica Gazzelloni

• 2022-03-03 Chapter 7: Moving Beyond Linearity - Jim Gruman
• 2022-03-10 Chapter 7: Moving Beyond Linearity (Lab) - Jim Gruman
• 2022-03-17 Chapter 8: Tree-Based Methods - Ricardo J. Serrano
• 2022-03-24 Chapter 8: Tree-Based Methods (Lab) - Ricardo J. Serrano
• 2022-03-31 Chapter 9: Support Vector Machines - Jenny Smith
• 2022-04-07 Chapter 9: Support Vector Machines (Lab) - Jenny Smith
• 2022-04-14 Chapter 10: Deep Learning - TBD
• 2022-04-21 Chapter 10: Deep Learning (Lab) - TBD
• 2022-04-28 Chapter 11: Survival Analysis and Censored Data - Michael Haugen
• 2022-05-05 Chapter 11: Survival Analysis and Censored Data (Lab) - Michael Haugen
• 2022-05-12 Chapter 12: Unsupervised Learning - TBD
• 2022-05-19 Chapter 12: Unsupervised Learning (Lab) - TBD
• 2022-05-26 Chapter 13: Multiple Testing - Federica Gazzelloni
• 2022-06-02 Chapter 13: Multiple Testing (Lab) - Federica Gazzelloni
• 2022-06-09 Final Discussion and Q&A - All

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How to Present

This repository is structured as a {bookdown} site. To present, follow these instructions:

1. Setup Github Locally
2. Fork this repository.
3. Create a New Project in RStudio using your fork.
4. Install dependencies for this book with devtools::install_dev_deps() (technically optional but it's nice to be able to rebuild the full book).
5. Create a New Branch in your fork for your work.
6. Edit the appropriate chapter file, if necessary. Use ## to indicate new slides (new sections).
7. If you use any packages that are not already in the DESCRIPTION, add them. You can use usethis::use_package("myCoolPackage") to add them quickly!
8. Commit your changes.
9. Push your changes to your branch.
10. Open a Pull Request (PR) to let us know that your slides are ready.

When your PR is checked into the main branch, the bookdown site will rebuild, adding your slides to this site.