Introduction to Machine Learning in R

Get started with Machine Learning

• What is Machine Learning?
• What are typical problems solved using ML?
• What are the different types of learning?

Machine Learning in R resources/background

• Explore R packages designed for Machine Learning
• Find out about framework packages such as {tidymodels}
• Brief introduction to deep learning and Kaggle competitions

Explore and prepare the data

• Load and prepare various datasets
• Define the problem

Design machine learning workflow

• Split datasets
• Introduction to regression and classification problems
• Specify a model and set its mode and arguments

Data Pre-process

• How to deal with categorical variables
• What to do when we have missing values

Machine Learning Algorithms

• What are sources of error
• Introduce the Decision Tree algorithm
• Introduce the Random Forest algorithm
• Introduce the Gradient Boosting algorithm

Resampling and Tuning

• What is cross-validation
• Perform hyper-parameter tuning

Regression and Clustering

• Solve a regression problem
• Carry out K-means clustering

Course participants are expected to have a good working knowledge of the R programming language. It is assumed that participants have some prior experience in basic data analysis (such as data manipulation and visualisation) and a basic understanding of statistics. No prior knowledge of machine learning theory is required.

All participants must have a computer (Windows, Macintosh) with current versions of R, R Studio and relevant packages pre-installed. If you have any problem installing them, please contact the course coordinator.
R-packages to install
install.packages(c(“tidyverse”, “tidymodels”))
install.packages(c(“glmnet”, “rpart”, “randomForest”))
install.packages(c(“xgboost”, “nnet”))
install.packages(c(“corrplot”, “rpart.plot”, “vip”))
install.packages(c(“parallel”, “doParallel”, “tidyclust”))

Online live sessions on March 21st, 28th and April 4th, 11th, 2025.

From 9:00 to 14:00 (Madrid time zone).

Total course hours: 25
20 hours of online live lessons, plus 5 hours of assignments.

This course is equivalent to 1 ECTS (European Credit Transfer System) at the Life Science Zurich Graduate School. The recognition of ECTS by other institutions depends on each university or school.

In the live sessions we will combine online lectures with hands-on computational exercises in R.

Live sessions will be recorded. However, attendance to the live sessions is required to obtain the course certificate.

This course will be delivered in English.