Training in Clinical Research (TICR)

University of California, San Francisco (UCSF)

BIOSTAT 216 - Machine Learning in R

This is a repository of assignments that I completed during a three-month course on machine learning methods using R. Each assignment is in the RMarkdown format. All required packages are listed in the {r setup} code block. Each homework is organized by folder and all data required for the analyses is given in the .csv file format in each folder. The final RMarkdown output of each knit .Rmd file is also uploaded as an .html file in each folder.

1. Homework 1: Regression
2. Homework 2: Classification methods and penalization
3. Homework 3: Maximum-margin classifiers, classification and regression trees, and bagging
4. Homework 4: Boosting, dimension reduction, and clustering
5. Homework 5: Using a pretrained neural network with torch and torchvision