Module Database Search
MODULE DESCRIPTOR | |||
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Module Title | |||
Edge Artificial Intelligence | |||
Reference | CM3603 | Version | 2 |
Created | February 2024 | SCQF Level | SCQF 9 |
Approved | July 2020 | SCQF Points | 15 |
Amended | April 2024 | ECTS Points | 7.5 |
Aims of Module | |||
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To develop Mobile Edge Computing applications using appropriate software and hardware platforms. |
Learning Outcomes for Module | |
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On completion of this module, students are expected to be able to: | |
1 | Explain the technologies, devices and applications which enable Mobile Edge Computing. |
2 | Review Edge AI applications and advantages over alternate architectures |
3 | Interpret the techniques and technologies used to support inference and training at the Edge |
4 | Point out challenges posed when implementing Edge AI systems and future developments needed to address them. |
5 | Formulate an Edge AI system to solve a real world problem according to specified requirements. |
Indicative Module Content |
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Mobile app development, prototyping, mobile design, data persistence, web services, JSON/XML, microservices, middleware stacks. |
Module Delivery |
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The module will be delivered through a mixture of lectures, tutorials and workshops; both online and on-campus thereby blending the delivery of the module. Edge Computing and related concepts will be introduced and illustrated through lectures. The understanding of the student would be tested, evaluated and further enhanced through an interactive series of tutorials in the lab sessions. In the tutorials, students will apply the theoretical knowledge to implement mobile applications by using an industry standard integrated development environment. |
Indicative Student Workload | Full Time | Part Time |
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Contact Hours | 48 | N/A |
Non-Contact Hours | 102 | N/A |
Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
TOTAL | 150 | N/A |
Actual Placement hours for professional, statutory or regulatory body |   |   |
ASSESSMENT PLAN | |||||
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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, 5 |
Description: | Individual Coursework assignment covering all learning outcomes. |
MODULE PERFORMANCE DESCRIPTOR | |
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Explanatory Text | |
The calculation of the overall grade for this module is based on 100% weighting of C1. An overall minimum grade of D is required to pass the module. | |
Module Grade | Minimum Requirements to achieve Module Grade: |
A | The student needs to achieve an A in C1. |
B | The student needs to achieve a B in C1. |
C | The student needs to achieve a C in C1. |
D | The student needs to achieve a D in C1. |
E | The student needs to achieve an E in C1. |
F | The student needs to achieve an F in C1. |
NS | Non-submission of work by published deadline or non-attendance for examination |
Module Requirements | |
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Prerequisites for Module | CM2602, CM2604 or equivalent. |
Corequisites for module | None. |
Precluded Modules | None. |
INDICATIVE BIBLIOGRAPHY | |
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1 | Parker, G.P. 2016. Platform Revolution: How Networked Markets are Transforming the Economy and How to Make Them Work for You. W.W. Norton & Company Ltd. |
2 | Buyya, R. and Srirama, S. 2019. Fog and Edge Computing: Principles and Paradigms. Wiley. |
3 | Domingos, P. 2015. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. 1st Edn. Penguin. |
4 | Mayer-Schönberger, V. and Cukier, K. 2013. Big Data: A Revolution that will Transform How we Live, Work and Think. John Murray. |