Module manager: Dr Arjan Gosal
Email: a.gosal@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2025/26
This module is not approved as an Elective
This module offers the opportunity to explore machine learning and deep learning applications in environmental data analysis specifically tailored for environmental data analysis. Students develop foundational knowledge while engaging in critical discussions about data misrepresentation and experimental design in environmental contexts. Emphasis is placed on the context-specific application of these technologies to large-scale environmental problems, fostering a comprehensive understanding of both technical and ethical dimensions of machine learning.
This module aims to enable students to:
1. Critically assess and select appropriate machine learning methodologies for addressing environmental data challenges.
2. Develop practical expertise in applying advanced machine learning techniques to environmental datasets, whilst considering ethical implications, robust interpretation of results, and transparent communication of findings.
3. Reflect deeply on the broader societal implications of machine learning applications in environmental science, recognising professional responsibilities when generating insights from environmental data.
/ Subject Specific Learning Outcomes
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
1. Critically evaluate various machine learning techniques, focusing on their applicability and limitations in environmental data analysis.
2. Implement machine learning and deep learning methods to address real-world environmental data challenges.
3. Distinguish between robust and misrepresentative outcomes in machine learning-based environmental studies.
4. Effectively communicate the process and results of machine learning analyses, limitations and potential impacts on environmental decision making
Skills Learning Outcomes
On successful completion of the module students will have demonstrated the following skills learning outcomes:
5. Digital and technical skills. Apply advanced machine learning methods to environmental datasets, demonstrating proficiency in selecting appropriate algorithms and evaluating their performance.
6. Work ready skills. Demonstrate effective time management and planning through meeting sequential deadlines; communicate complex machine learning concepts and analytical findings clearly to diverse audiences using appropriate visualisations and technical language.
7. Sustainability skills. Apply systems thinking to evaluate machine learning outputs and their implications for sustainable development; develop data-driven recommendations addressing environmental challenges aligned with SDGs.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lectures | 8 | 1 | 10 |
| seminars | 3 | 1.5 | 4.5 |
| Practicals | 5 | 2 | 10 |
| Private study hours | 125.5 | ||
| Total Contact hours | 24.5 | ||
| Total hours (100hr per 10 credits) | 150 | ||
Formative feedback will be available to students in the practical computer labs, where staff and demonstrators will be present. In addition, common issues encountered and how to overcome them communicated to students post session via Minerva. The seminars are purely designed to allow critical discussion around key topics, and formative feedback will be given in terms of both content, but also how to critically evaluate.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Coursework | Coursework | 50 |
| Coursework | Coursework | 50 |
| Total percentage (Assessment Coursework) | 100 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
The reading list is available from the Library website
Last updated: 23/04/2025
Errors, omissions, failed links etc should be notified to the Catalogue Team