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 the definition of machine learning and its essential terminology; identify potential areas of its application in healthcare; describe the evaluation of classification models.

2) Describe hyperparameter tuning and evaluation of a classifier.

3) Present the foundational concepts of probabilistic learning such as Bayes’ theorem.

4) Explain the principles of decision tree learning and apply it to health data.

5) Articulate the underlying concepts of artificial neural networks and apply them to a multidimensional feature space of health data.

6) Describe the importance and key concepts of data pre-processing and dimensionality reduction and apply them to a feature space of health data.

7) Define the theoretical principles of support vector machines and apply these methods to health data.

8) Contrast different classifiers and construct an ensemble classifier for decision making.

9) Discuss latest developments in the area of deep learning.

You will learn though a combination of lectures, discussion and computer-based practicals using Python.

Mitchell, T. (1997) Machine Learning. McGraw-Hill, Inc. New York.

Abu-Mostafa, Y. S., Magdon-Ismail, M., & Lin, H. (2012). Learning from data: a short course. [United States]: AMLBook.com.

Esteban, C., Arostegui, I., Garcia-Gutierrez, S., Gonzalez, N., Lafuente, I., … Quintana, J. M. (2015). A decision tree to assess short-term mortality after an emergency department visit for an exacerbation of COPD: a cohort study. Respiratory Research, 16(1).