These are the materials for our BU Machine learning workshop at Boston University on 12/11/2023. Hopefully they are helpful!
- Lots of diverse material and new concepts will be covered in this course
- Machine Learning is NOT a spectator sport! You need to practice the skills you learn over and over again!
- Communication: if you have questions or concerns, please email us: [email protected] or [email protected]
- Materials for the course:
- All materials for this course will be posted on the course GitHub page: https://github.com/wevanjohnson/2023_12_BU_ML_Workshop/edit/main/README.md
- You obviously have this since you are here!
- Dependencies (R packages to install and datasets to download):
- Please install the following R packages for Data Management:
tidyverse,DT,gridExtra,dslabs(Rafa Irizarry’s datasets for his Data Science book!). In R:install.packages(c("tidyverse", "DT", "gridExtra", "dslabs")) - Please install the following R packages for Machine learning:
caret,e1071,rpart,randomForest,neuralnet. In R:install.packages(c("caret", "e1071", "rpart", "randomForest", "neuralnet")) - You will need the following datasets: ESL.mixture.rda, TBnanostring.rds
- Please install the following R packages for Data Management:
- Schedule:
| Topics |
|---|
| Introduction to Machine Learning Terminology |
| Overview of Machine Learning Methods |
Data preparation (the caret package in R) |
| Supervised and unsupervised learning |
| Kernel-based Methods (particularly SVMs) |
| Decision Trees and Random Forests |
| Neural Networks |
| Code + case studies are integrated throughout |