Key information
- Faculty
- Faculty of the Built Environment
- Teaching department
- Bartlett School of Environment, Energy and Resources
- Credit value
- 15
- Restrictions
-
This module is compulsory for students taking MSc Sustainable Heritage (Data Science).
Limited spaces for Masters level students from programmes other than MSc Sustainable Heritage.
Limited space for auditing PhD students (subject to module lead approval).
- Timetable
-
Alternative credit options
There are no alternative credit options available for this module.
Machine Learning is a powerful method of using data to make predictions. The applications to the heritage sector are innumerable, ranging from heritage recognition and identification, to managing archives and repositories. This project-based module will offer an introduction to the different forms of machine learning strategies (Linear Regression, Logistic Regression and Neural Networks), alongside the opportunity to apply and further explore these tools through a practical project rooted in heritage practice.
Module deliveries for 2024/25 academic year
Intended teaching term:
Term 2 听听听
Postgraduate (FHEQ Level 7)
Teaching and assessment
- Mode of study
- In person
- Methods of assessment
-
100%
Coursework
- Mark scheme
-
Numeric Marks
Other information
- Number of students on module in previous year
-
32
- Module leader
-
Dr Josep Grau-bove
- Who to contact for more information
- bseer-studentqueries@ucl.ac.uk
Intended teaching term:
Term 2 听听听
Undergraduate (FHEQ Level 7)
Teaching and assessment
- Mode of study
- In person
- Methods of assessment
-
100%
Coursework
- Mark scheme
-
Numeric Marks
Other information
- Number of students on module in previous year
-
0
- Module leader
-
Dr Josep Grau-bove
- Who to contact for more information
- bseer-studentqueries@ucl.ac.uk
Last updated
This module description was last updated on 8th April 2024.
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