Boston University Machine Learning Workshop

These are the materials for our BU Machine learning workshop at Boston University on 12/11/2023. Hopefully they are helpful!

Things you should know about this course

• 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: w.evan.johnson@rutgers.edu or vkola@bu.edu
• 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
• 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 ​