Introduction to Machine Learning (with R)

This repository regroups works on the "introduction to machine learning" course of the SIB. This is the R implementation of this course, and we also propose a python implementation

pre-requisites

The course is targeted to life scientists who are already familiar with the R programming language and who have basic knowledge on statistics.

In order to follow the course you need to have installed R and Rstudio.

You will also need to have installed the following packages:

from CRAN (install with install.packages()):

• BiocManager
• dendextend
• dplyr
• ggplot2
• GGally
• ggbiplot
• gplots
• gridExtra
• important
• pheatmap
• ranger
• RColorBrewer
• stringr
• tidyr
• tidymodels
• recipes
• rpart.plot
• themis
• vip
• umap

from Bioconductor (install with BiocManager::install)

• bluster

course organization

The course is organized in several, numbered, Rmarkdown documents, each corresponding to a chapter which interleaves theory, code demo, and exercises.

Chapter1 : Exploratory analysis
Chapter2 : Unsupervised learning
Chapter3 : Supervised learning and Machine Learning routine
Chapter4 : Distance-based model for classification
Chapter5 : Decision trees for classification
Chapter6 : Machine Learning for regression

Solutions to each practical can be found in the solutions/ folder.

directory structure

• data : contains the datasets
• exam : contains the data and instruction of a facultative exam
• images : images generated or used in the notebooks
• R_notebooks : folder containing the Rmarkdown chapters