Key information
- Faculty
- Faculty of Engineering Sciences
- Teaching department
- Medical Physics and Biomedical Engineering
- Credit value
- 15
- Restrictions
-
This module requires students to have a strong background in mathematics and some programming experience.
For the maths background, we recommend you have familiarity with linear algebra, calculus and probability theory.
From the programming side, we recommend you have familiarity with programming (can be Python or another programming language, e.g. MatLab.) The module uses Python and mainly the sklearn library.
- Timetable
-
Alternative credit options
There are no alternative credit options available for this module.
Machine learning (ML) and artificial intelligence (AI) is ubiquitous and finds its application in various fields in science and healthcare. In this course we will introduce the underlying mathematics and basic concepts of machine learning including regularized linear models, tree-based models, support vector machines, ensemble methods, neural-networks as well as model assessment. The methods are illustrated and introduced using problems relevant to medical imaging: image reconstruction, image enhancement (noise reduction, super-resolution, image quality transfer), modality transfer, image registration and segmentation, and image-based diagnosis (classification and regression). At the end of course, students will have an overview on relevant methods, their advantages and limitations as well as how to apply them in their field.
Module deliveries for 2024/25 academic year
Intended teaching term:
Term 1 听听听
Postgraduate (FHEQ Level 7)
Teaching and assessment
- Mode of study
- In person
- Methods of assessment
-
50%
Coursework
50%
Other form of assessment
- Mark scheme
-
Numeric Marks
Other information
- Number of students on module in previous year
-
36
- Module leader
-
Dr Andre Altmann
- Who to contact for more information
- medphys.teaching@ucl.ac.uk
Intended teaching term:
Term 1 听听听
Undergraduate (FHEQ Level 7)
Teaching and assessment
- Mode of study
- In person
- Methods of assessment
-
50%
Coursework
50%
Other form of assessment
- Mark scheme
-
Numeric Marks
Other information
- Number of students on module in previous year
-
19
- Module leader
-
Dr Andre Altmann
- Who to contact for more information
- medphys.teaching@ucl.ac.uk
Last updated
This module description was last updated on 8th April 2024.
听
Close
