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 Smart Buildings and Digital Engineering.
Three spaces are reserved for MEng EAD students and a limited number are reserved to EDE students. This module is available to a limited number of IEDE PhD students subject to there being spaces available. Please note that we may only be able to confirm these spaces towards the end of the module selection period
- Timetable
-
Alternative credit options
There are no alternative credit options available for this module.
The module focuses on the applications of Machine Learning towards improving building operation.
Through a series of case studies, this module will introduce you to applications of machine learning and the potential of such models to making buildings smarter. The case studies will draw upon applications in areas like occupant modelling, performance prediction, building services and their control.
Through the lens of these case studies relevant machine learning algorithms and tools will be presented to provide grounding on:
- Machine learning model-development basics (hyperparameters, validation sets, overfitting, underfitting)
- Regression (e.g. Support Vector Machine, Gaussian Processes)
- Clustering (e.g. k-means clustering)
- Reinforcement learning
- Advanced topics (deep neural networks, convolutional neural networks)
Module deliveries for 2024/25 academic year
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 Rui Tang
- Who to contact for more information
- bseer-studentqueries@ucl.ac.uk
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
-
19
- Module leader
-
Dr Rui Tang
- 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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