Computer Vision for The Energy Sector
Explore machine learning and computer vision for image/video analysis with real case studies.
Topics
On completion of this module, students are expected to be able to:
- Critically appraise the challenges posed by the management and processing of complex image and/or video-based datasets.
- Demonstrate an understanding of the main concepts of computer vision and how machines "see".
- Critically evaluate and select state-of-the-art methods to extract features from the input data and detect, localise, recognise or classify the information or phenomena depicted.
- Discuss solutions to diverse case studies from real-life applications in the Oil & Gas and renewable sectors.
The indicative content covered in this course includes:
- Main concepts of computer vision
- Data in the energy sector
- Data acquisition and storage
- Data pre-processing and cleaning
- Machine learning principles
- Image manipulation
- Basic and advanced models for image classification, detection, recognition and segmentation
- Real-life use cases
- Tools and libraries (e.g. Python, OpenCV, Scikit Learn, cloud services, etc.)
- Sharing code and working remotely & effectively (online notebooks and repositories e.g. GitHub, Kaggle, Colab, etc.).
RGU Upskilling
Undertaking a RGU online short course can help you to develop or change your career or support your business to grow.
We're proud to offer a range of online short courses tailored to meet the evolving needs of businesses and individuals both locally, nationally and in the global community. Additionally, if you are domiciled in Scotland, you may be eligible for a fee-waiver place, which means you could study for free. Identified and created in collaboration with industry, these credit-bearing online courses are designed to enhance employability for individuals and organisations looking to upskill their workforce.
Find out more about the range of courses on offer and the benefits of studying online with RGU:
Teaching
Teaching/learning activity as follows:
- Live Lectures: 1 hour/week
- Live practical sessions for tutorial exercises: 2 hours/week
- Tutorial exercises: a range of guided exercises to help participants further explore the principles covered in lectures.
Assessment
- A project applying techniques of computer vision to a dataset and presenting the analysis and conclusions in the form of an interactive report with code (Jupyter Notebook).
Independent Study
- Materials and exercises are available online, allowing participants to study flexibly and independently at time and place to fit around existing work and life commitments.
- Further reading resources.
- Online tutor support.
Staff Delivering on This Course
The course team is comprised of experienced academics who won multiple STAR awards and have worked on multiple research and consultancy projects in the field of machine learning and computer vision. Guest lectures showcasing real-life success stories will be delivered by industry partners.
Find out moreRGUflex
There are no prerequisites for this course, however some programming experience is preferred.
Academic Year 2025/2026
Fee-waiver places
If you are domiciled in Scotland, you may be eligible for a fee-waiver place, meaning you can study for free.
To meet the requirements to be allocated a funded place, applicants must meet the criteria to be assessed as "Home" for the purposes of fees. Find out more about the Fee Status and the Scottish Government regulations that we use to make our assessment:
Eligibility is assessed on application and further evidence may be required.
All other applicants
Applicants who are not eligible for a fee-waiver place will be required to meet the module cost to study:
- Course fee - £520 for entire module
Additional Costs
Some other costs are not included in the course fees:
Disclaimer
For new intakes, course fees are reviewed and published annually for each mode of delivery. Tuition fees are fixed for the duration of a course at the rate confirmed in the offer letter. For further information:
Any Questions?
Get in touch with the team and we'll do our best to help.