Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Computer Vision for the Energy Sector | |||
| Reference | CMM560 | Version | 1 |
| Created | November 2022 | SCQF Level | SCQF 11 |
| Approved | January 2023 | SCQF Points | 15 |
| Amended | ECTS Points | 7.5 | |
| Aims of Module | |||
|---|---|---|---|
| To provide students with knowledge of the latest advances in artificial intelligence, machine learning and data science, used in the complex and time-consuming task of image/video analysis. Students will be introduced to the fundamentals and the latest trends in computer vision, which will be applied to real-life problems faced by the oil and gas and the renewables sectors. | |||
| Learning Outcomes for Module | |
|---|---|
| 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 and gas and renewable sectors. |
| Indicative Module Content |
|---|
| 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.). |
| Module Delivery |
|---|
| The module is delivered using a combination of online self-study materials, directed reading and activities and supported using virtual workshops and tutor support. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | N/A | 20 |
| Non-Contact Hours | N/A | 130 |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | N/A | 150 |
| Actual Placement hours for professional, statutory or regulatory body |
| ASSESSMENT PLAN | |||||
|---|---|---|---|---|---|
| If a major/minor model is used and box is ticked, % weightings below are indicative only. | |||||
| Component 1 | |||||
| Type: | Coursework | Weighting: | 100% | Outcomes Assessed: | 1, 2, 3, 4 |
| Description: | 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). | ||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| Explanatory Text | |
| The calculation of the overall grade for this module is based on 100% weighting of C1. An overall minimum grade D is required to pass the module. | |
| Module Grade | Minimum Requirements to achieve Module Grade: |
| A | The student needs to achieve an A in Component 1. |
| B | The student needs to achieve a B in Component 1. |
| C | The student needs to achieve a C in Component 1. |
| D | The student needs to achieve a D in Component 1. |
| E | The student needs to achieve an E in Component 1. |
| F | The student needs to achieve an F in Component 1. |
| NS | Non-submission of work by published deadline or non-attendance for examination |
| Module Requirements | |
|---|---|
| Prerequisites for Module | None. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | Kar, K., 2020, Mastering Computer Vision with TensorFlow 2.x: Build Advanced Computer Vision Applications Using Machine Learning and Deep Learning Techniques. Packt. |
| 2 | Geron, A., 2019, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reily. |
| 3 | Howse, J. and Minichino, J., 2020, Learning OpenCV 4 Computer Vision with Python 3: Get to grips with tools, techniques, and algorithms for computer vision and machine learning, 3rd Edition. Packt. |
| 4 | Varga, E., 2019. Practical Data Science with Python 3: Synthesizing Actionable Insights from Data. Berkeley, CA: Apress. |
| 5 | Chollet, F., 2018, Deep Learning with Python. Manning. |
| 6 | Elgendy, M., 2020, Deep Learning for Vision Systems. Manning. |