Introduction to AI and machine learning for biomedical research

Get acquainted with AI models and discover how machine learning is transforming biomedical research! For the fourth time, VAIA and its partners are offering this successful introductory training – this year organized in collaboration with the Doctoral Schools of Hasselt University. Today’s biomedical research generates vast amounts of data – from medical images and omics-data to electronic health records. Machine learning provides powerful tools to uncover patterns, classify complex datasets and support evidence-based decisions. From analyzing CT scans in cancer diagnostics to identifying tailored treatments for MS patients, this course will guide you through the fundamentals of AI and its cutting-edge applications in biomedicine.

You can find all practical details about the course on the VAIA webpage – make sure to check it out! 

Learning outcomes

After having attended this workshop, you will...

• have an overview of the terminology and algorithms of AI that are applied in current research,
• be aware of the possibilities as well as the challenges concerning AI techniques (bias, fairness, data management, ethics, etc.),
• be able to discuss problems related to their research with an AI expert using appropriate terminology,
• beable to judge if AI techniques might be interesting to apply in their research,
• be familiar with some contemporary research topics in the biomedical field that use AI-related techniques. 

Competences

An important part of preparing for any further professional step is becoming (more) aware of the competences you have developed and/or want to develop. In the current workshop, the following competences from the UHasselt competency overview are actively dealt with:

• academic research competences:
  • research methods
  • subject knowledge

For whom?

• researchers in life sciences

When and where?

• Part 1: Self-paced online course KULeuven: ‘AI in Healthcare. Hype or Help’ (Chapters 1, 2 and 4 are mandatory. Chapter 9 is recommended.)
• Part 2: Live online classes
  • Module 1: Supervised Learning
    18 november, 13:00-15:00
    Axel-Jan Rousseau (UHasselt, VAIA) & Christos Chatzichristos (KU Leuven, VAIA)
  • Module 2: Unsupervised Learning
    20 november, 13:00-15:00
    Celine Vens (KU Leuven)
  • Module 3: Deep Learning and Neural Networks
    25 November, 13.00-15.00
    Dirk Valkenborg (UHasselt)
  • Module 4: Reinforcement Learning
    27 November, 13.00-15.00
    Pieter Libin (VUB AI Lab)
  • Module 5: Fairness and Privacy of AI
    2 December, 13.00-15.00
    Marybeth Defrance (UGent)
• Part 3: Presentation of use cases (online)
  Module 6: Use cases from the biomedical sector
  5 December, 13.00-15.00

Registration?

• Attendance at 5 of the 6 modules is necessary to receive a certificate of attendance!
• UHasselt researchers:
  • Register here from August 22 until November 03, 2025. If you miss this deadline, late registration may be possible by sending an email to doctoralschools@uhasselt.be (before November 15, 2025).
  • Please cancel your registration at least one week in advance in case you cannot make it (cf. cancellation & no-show policy). 
• Other researchers:
  • Register here from August 22 until November 14, 2025. 

Acknowledged as?

• DS HLS: scientific skills - one technical skills course

In valued collaboration with: