91猎奇

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.).

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 91猎奇

• 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.