Computer Vision for The Energy Sector
Explore machine learning and computer vision for image/video analysis with real case studies.
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.).
The Data Lab
The development of this course has been funded by The Data Lab
In partnership with the Scottish Funding Council (SFC), our online upskilling short courses have been developed in response to feedback from businesses regarding their people and skills needs and are therefore helpful for individuals considering their employment options as well as organisations looking to upskill their employees. Find out more:
Modules and delivery order may change for operational purposes. The University regularly reviews its courses. Course content and structure may change over time. See our course and module disclaimer for more information.
10 weeks of 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.
- 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).
- 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.
The Inclusion Centre advises and supports students who disclose a sensory or mobility impairment, chronic medical condition, mental health issue, dyslexia and other specific learning differences. Applicants are encouraged to arrange a pre-entry visit to discuss any concerns and to view the facilities.
Online Learning & Support
All online learning students, benefit from using our collaborative virtual learning environment, CampusMoodle. You will be provided with 24/7 online access to your learning material and resources, along with the ability to interact with your class members and tutors for discussion and support.
Study Skills Support
The Study Support Team provides training and support to all students in:
- Academic writing
- Study skills (note taking, exam techniques, time management, presentation)
- Maths and statistics
- English language
- Information technology support
The Library offers support for your course, including the books, eBooks, and journals you will need. We also offer online reading lists for many modules, workshops and drop-ins on searching skills and referencing, and much more.
There are no prerequisites for this course, however some programming experience is preferred.
Academic Year 2023/2024
- Course fees will be met in full for students who qualify for Scottish Funding Council funding. To qualify for SFC funding, applicants must be resident in Scotland.
- £350 entire course - Applicants who are not eligible for SFC funding or are currently receiving SAAS/SFC funding for other courses.
The following course-related costs are not included in the course fees:
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 see: