UCL Institute of Health Informatics


Machine Learning in Healthcare and Biomedicine

The module provides an introduction into the principles of machine learning in healthcare and biomedicine, covering the key concepts involved in designing and evaluating approaches to machine learning. It will provide a practical introduction to common approaches to machine learning, so that students acquire experience in using different machine learning algorithms and concepts (i.e. decision trees, probabilistic classifiers, support vector machines, artificial neural nets, and ensembles) in the context of healthcare. As a pre-condition for this module you must have accomplished the two modules: (1) Scientific Software Development with Python in Health Research (SSDPHR) and (2) Principles of Health Data Science (PHDS).

Module code


UCL credits


Course Length

9 Weeks

Face to Face Dates

14:00 - 17:00

Jan: 12, 19, 26

Feb: 02, 09, 23

Mar: 02, 09, 16, 23

Assessment Dates


Module organiser

Dr Holger Kunz Please direct queries to courses-IHI@ucl.ac.uk


    • Machine learning
    • Hyperparameter tuning and evaluation
    • Probabilistic learning
    • Decision tree learning
    • Artificial neural netw
    • Data pre-processing and dimensionality reduction
    • Support vector machines
    • Ensemble classifier 
    • Deep learning

      Teaching and learning methods

      This 15 credit module lasts for 10 weeks and should represent 150 learning hours. The module will use a mixture of lectures and computer-based practical using Python. There will be private reading and materials will be made available via Moodle, with some online activities.


      The final assessment will involve analysing an example data set and writing a report to answer the given research questions.