• Advanced Courses in Life Sciences

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Live Online Course – 1st Edition

Introduction to Machine Learning in R

September 6th-21st, 2024

Live sessions will be recorded

Course Introduction to machine learning in R

Course overview & Programme

Machine Learning is an extremely popular topic within the field of Artificial Intelligence. We encounter the results of machine learning algorithms on a daily basis, for example, when we shop online, play mobile games, applying for an insurance or even “driving” a driver-less car.

The aim of the course is to introduce participants to the main components for implementing Machine Learning in R using the {tidymodels} and {tidyverse} framework packages. By the end of the course, students will be able to perform the necessary tasks for machine learning such as defining the problem, prepare and pre-process data, and apply different machine learning algorithms such as Extreme Gradient Boosting, Random Forests etc. In addition, we explore how to fit a model and evaluate its performance as well as measuring the accuracy of model predictions.

This course includes a range of activities such as model building demos, live-coding sessions, interactive quizzes, and practical exercises to work individually or in a group. Active participation and contribution are highly recommended and encouraged.

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”))

The use of webcam and headphones is strongly recommended, and access to a good internet connection is required.


Nicolas Attalides instructor for Transmitting Science

Dr. Nicolas Attalides

Dates & Schedule

September 6th-21st, 2024

9:30 to 13:00 (Madrid time zone)

Online live sessions on

Fridays and Saturdays

6th, 7th, 13th, 14th, 20th, and 21st of September

The rest of the time will be taught with recorded classes and assignments, to be done between the live sessions.

Total course hours: 35

31 hours of online live lessons, plus 4 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.



This course will be delivered live online

This course will be taught using a combination of live (synchronous) sessions on Zoom and tasks to be completed in between live sessions on the Slack platform.

Live sessions will be recorded. Recordings will be made available to participants for a limited period of time. However, attendance to the live sessions is required.


Places are limited to 15 participants and will be occupied by strict registration order.

Participants who have completed the course will receive a certificate at the end.

Haris Saslis instructor for Transmitting Science

Dr. Haris Saslis
Transmitting Science

Soledad De Esteban-Trivigno Transmitting Science coordinator

Dr. Soledad De Esteban-Trivigno
Transmitting Science

Fees & Discounts

  • Course Fee
  • Early bird (until June 30th, 2024):
  • 620 €
    (496 € for Ambassador Institutions)
  • Regular (after June 30th, 2024):
  • 692 €
    (553.60 € for Ambassador Institutions)
  • Prices include VAT.
    After registration you will receive confirmation of your acceptance on the course.
    Payment is not required during registration.

We offer discounts on the Course Fee.

Discounts are not cumulative. Participants receive the highest appropriate discount.

We also offer the possibility of paying in two instalments. Please contact us to request this.

Former participants of Transmitting Science courses receive a 5% discount on the Course Fee.

20% discount on the Course Fee is offered to members of certain organisations (Ambassador Institutions). If you wish to apply for this discount, please indicate it in the Registration form (proof will be asked later). If you would like your institution to become a Transmitting Science Ambassador Institution, please contact us at communication@transmittingscience.com

Unemployed scientists, as well as PhD students without any grant or scholarship to develop their PhD, can benefit from a 40% discount on the Course Fee. This applies only to participants based in Spain. If you wish to ask for this discount, please contact us. The discount may apply for a maximum of 2 places, which will be covered by strict registration order.


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