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Machine Learning Workshop

Using data science and machine learning for infection science: A hands-on introduction 
Friday, 17 April 2026, 9:00 - 16:30 CEST

Now in its third consecutive year, this workshop has established itself as an introductory forum for exploring the role of data science and machine learning in infection science. The increasing availability of complex clinical and laboratory data presents significant opportunities to improve infection science, microbiology, and healthcare delivery. Data science and machine learning offer powerful approaches to analyse such data, uncover patterns, and support evidence-based decision-making.

This workshop will provide a structured introduction to the principles and practice of data science and machine learning in the context of infection science. Through a series of guided, hands-on sessions, participants will gain experience with preparing and exploring data, applying core data science methods for analysis and visualisation, and building and evaluating machine learning models.

The workshop is designed for those with all backgrounds, who wish to develop foundational knowledge and practical skills in this area. No prior experience with programming or machine learning is required, as we will be using a no code tool. 

Participants will leave with an improved understanding of how data-driven methods can be applied to infection science and with the confidence to begin exploring these approaches within their own work.

Instructor: Benjamin McFadden (University of Western Australia, Western Australian Department of Health)

Download the workshop programme

Learning outcomes

The intended learning outcomes for individuals present at the workshop include: 

  • Understanding the “end-to-end” lifecycle of a machine learning project
  • Understanding when and when not to use machine learning approaches
  • Hands on practical experience with machine learning using no-code and/or code tools and technologies. Provide attendees with the confidence to explore machine learning in their own contexts, in addition to providing attendees with resources for further exploration and use.
  • Understanding how to structure a machine learning research project, providing attendees with the information required to ensure that their projects are publication ready. 

Registration

Registration category

Fees

ESCMID member

EUR 150

Non-member

EUR 200

Congress registration is required to book a spot in the machine learning workshop. Registration to the workshop includes teaching material and coffee breaks. Travel and accommodation is not be included. Participants can register for the Machine Learning Workshop starting on 29 October 2025 as part of the ESCMID Global 2026 registration process. 

All cancellations must be sent to escmidregistration(at)escmid.org and follow the general registration cancellation policy. 

The organisers reserve the right to cancel the course up to two weeks before the start date. A full refund of the course fees will be allowed, but the organisers cannot be held responsible for any other costs incurred (transports, hotels, etc.).

For information regarding the content of the workshop, please reach out to benjamin.mcfadden(at)research.uwa.edu.au

For questions relating to registration and administration please use the contact form below. 

FAQ

Individuals from all areas of infectious disease and infection science interested in learning more about data science and machine learning. No prior background in data science and machine learning is assumed and the workshop is suitable for beginners. 

  1. Participants will develop an understanding of the “end-to-end” lifecycle of a machine learning solution.
  2. Participants will develop a fundamental understanding of machine learning and data science.
  3. Participants will learn how to develop their own machine learning workflow and models with no-code tools.
  4. Everyone will receive copies of all the workshop material, in addition to supplementary material including more machine learning examples so participants can further explore the field and develop their understanding beyond the workshop.

The hands-on sections will involve participants using their own laptops, with the orange data mining software, which is free and open source and can be downloaded from https://orangedatamining.com/. More details will be provided to participants 8 weeks, 4 weeks, and 1 week before the workshop. 

Yes, a more detailed curriculum is provided as follows:

  • Participants will be Introduced to data science and machine learning. Examples of machine learning applied for infection science will be discussed and we will provide sufficient background to support the hands-on components of the workshop.
  • We will then proceed to discuss the "end-to-end" machine learning lifecycle. We will explore the key principles of building machine learning solutions, the types of machine learning problems, and when to use machine learning and when not to use it. We will discuss preparing and managing data for machine learning, training machine learning models, evaluating machine learning models, and briefly discuss other areas such as deployment, monitoring, and continual learning.
  • Hands on session 1 will introduce participants to the software that we will be using (Orange data mining software), which is open source and free to download and use.
  • Hands on session 2 will be about cleaning and preparing data for machine learning, and we will also discuss feature engineering.
  • This will flow into hands on session 3 where we will start training different machine learning models and start implementing strategies for improving machine learning model performance.
  • Hands on session 4 will focus on evaluation of our machine learning models, discussing how we can effectively evaluate machine learning performance, and the importance of correct machine learning model evaluation.
  • Hands on session 5 will examine the important considerations for implementing machine learning in practice, and we will test our models with new data and retrain the models to try and improve performance.
  • Towards the end of the workshop, I will provide detail about how to report machine learning experiments, with the goal of improving the quality of research being submitted for publication.

There will be time for questions, open discussion, technical support, and networking.

Contact

Looking for more information? Get in touch with an ESCMID representative today!

Contact form