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Advanced Machine Learning

Module code: 7MRI0010
Module credits: 15
Module convenors: Dr Jorge Cardoso



To educate students with regards to novel artificial intelligence algorithms for the analysis and predictive modelling of multiple types of healthcare data such as medical images, genetics, clinical/epidemiological variables, and free text.

The module provides the theoretical understanding and practical implementation of models that are at the core of advanced analysis and actionability of medical data.


Learning Outcome

The module will utilize a mixture of lectures, required reading with associated seminars, tutorials, and labs.

The lab sessions and tutorials will be used to expose students to the real-world need and limitations of advanced models. From applying simple feed-forwards convolutional neural networks in a supervised setting, to training uncertainty-aware complex multi-task networks from multiple sources of data.

The core material will include, but are not limited to the following:

  • New representation learning architectures and loss functions
  • Adversarial learning in a medical setup
  • Attention and auxiliary tasks as a form of regularisation.
  • Model interpretability and introspection
  • Uncertainty estimation using Bayesian deep learning
  • Reinforcement learning in medicine
  • Sequence and predictive modelling
  • Natural language processing and language encoding
  • Multi-modal predictive models
  • Validation of AI models in healthcare

Students will work on the assignments both individually and in groups, but reports will be submitted independently. This guarantees that each student will learn the fundamental concepts as will be assessed in the report, but they will be able to collaborate towards producing a working implementation of complex algorithms. Tutor support will be organized with associated workshops.

This module will run throughout semester 2 and links well with the Machine Learning for Biomedical Applications module of semester 1.


Contact time, hours of study, e-learning
Lectures (hrs) 20
Seminars/Tutorials (hrs)  
Field/laboratory/studio/supervised learning (hrs) 30
Project work (hrs) 30
Placement (hrs)  
Self guided learning (hrs) 70
E-learning Mark scheme and assessment details


Mark scheme and assessment details
Assessment typePercentage of final gradeAssessment titleMark schemeQualifying mark
Coursework 40 Extended problem solving and report 2500 words CTTA50 40
Coursework 60 Lab report 3000 words CTTA50 40


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