Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Cloud AI | |||
| Reference | CM4133 | Version | 1 |
| Created | September 2023 | SCQF Level | SCQF 10 |
| Approved | April 2024 | SCQF Points | 15 |
| Amended | ECTS Points | 7.5 | |
| Aims of Module | |||
|---|---|---|---|
| Provide students with the necessary technical skills and underlying knowledge that will enable them to examine and utilise cloud-based AI services for use within client applications. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Critique a range of AI services with respect to use within client applications with respect to impact on users. |
| 2 | Communicate social and ethical issues associated with using AI in mobile and web settings. |
| 3 | Develop client-server systems incorporating AI services into client applications. |
| 4 | Compose solutions for the use of AI to mine data and extract knowledge within applications. |
| Indicative Module Content |
|---|
| Machine Learning techniques: supervised learning, unsupervised learning, multi-tier client/server architectures, web services, AI model performance and scalability, data integrity, security, and privacy, |
| Module Delivery |
|---|
| Key concepts and introduced and illustrated through lectures, with students developing competencies in practical understanding and development during lab sessions. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | 30 | N/A |
| Non-Contact Hours | 120 | 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 | |||||
|---|---|---|---|---|---|
| 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 practical coursework involving the well considered integration of cloud-based AI services into a working application. | ||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| Explanatory Text | |
| The calculation of the overall grade for this module is based on 100% weighing 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 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 | |
|---|---|
| Prerequisites for Module | None. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | Gupta, Pramod, and Sehgal, Naresh Kumar. Introduction to machine learning in the cloud with Python : concepts and practices. Springer 2021. |
| 2 | Walsh, Barry. Productionizing AI: How to Deliver AI B2B Solutions with Cloud and Python. Springer. 2022. |
| 3 | Bratis, Irene. The AI Product Manager's Handbook: Develop a product that takes advantage of machine learning to solve AI problems. Packt Publishing 2023. |