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Description
This module will provide you with an introduction to the principles of machine learning in healthcare and biomedicine, covering the key concepts involved in designing and evaluating approaches to machine learning. The module will focus on applied methods for problems in prevention, diagnosis, therapy, aetiology, and prognosis. You will be given a practical introduction to common approaches, offering you 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.
At the end of this module you will be able to:
1) Outline Artificial Intelligence in Healthcare and its essential terminology; identify potential areas of its application in healthcare
2) Describe hyperparameter tuning and evaluation
4) Articulate the underlying concepts of artificial neural network
5) Describe the key concepts of data pre-processing and dimensionality reduction
6) Explain the principles of tree-based algorithms and apply it to health data
7) Describe the key concepts of ensemble-based algorithms
8) Explain and apply the principles of unsupervised learning on datasets in healthcare
9) Discuss latest developments in deep learning and AutoML
You will learn though a combination of lectures, discussion and computer-based practicals using Python.
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Mitchell, T. Machine Learning. McGraw-Hill, Inc. New York.
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 8th April 2024.
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