Computer Vision for The Energy Sector level 11

Program Overview

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The aim of this course is to provide students with knowledge of techniques in machine learning and computer vision applied to the complex and time-consuming task of image/video analysis. The course is developed in partnership with industrial collaborators in the Energy sector to provide students with real-life case studies.

Program Outline

Topics

On completion of this module, students are expected to be able to:

1. Critically appraise the challenges posed by the management and processing of complex image and/or video-based datasets.
2. Demonstrate an understanding of the main concepts of computer vision and how machines "see".
3. 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.
4. 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.).

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The Data Lab

The development of this course has been funded by

The Data Lab

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Upskilling Courses

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:

Upskill with our online short courses

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Disclaimer

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.

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Teaching

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.

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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).

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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.

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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.

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Academic Support

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.

The Inclusion Centre

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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.

CampusMoodle

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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

Study Skills Support

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Library 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.

University Library