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Anglia Ruskin University: Embedded Computing and Machine Learning
| Institution | Anglia Ruskin University |
|---|---|
| Department | School of Computing and Information Sciences |
| Web | http://www.aru.ac.uk |
| answers@aru.ac.uk | |
| Telephone | 01245 68 68 68 |
| Study type | Taught |
Summary
Advance your career in embedded systems and artificial intelligence (AI) with our 100% online Master’s degree in Embedded Computing and Machine Learning.
Enhance your understanding of embedded systems and artificial intelligence (AI) with our Master’s degree in Embedded Computing and Machine Learning. Study part-time by distance learning and develop the skills you need to harness the power of machine learning applications in various industrial contexts.
Our Embedded Computing and Machine Learning MSc will give you the opportunity to explore the industry trends where big chip designing and manufacturing multinational companies are emphasising embedded and portable devices optimised for machine learning at the edge.
During the first two modules, you'll gain skills to leverage Arm technologies and develop intelligent, distributed, heterogeneous, and secure solutions. You'll also expand your knowledge and skills in advanced topics of machine learning and AI, such as deep learning, generative AI and their applications to prompt engineering.
You'll crown your work on this course with a final year project which provides you with a platform to showcase the acquired skills and knowledge in an application domain of interest.
Embedded computing, especially when paired with machine learning, promises to provide the tools to enhance technology, business models and decision-making across a range of sectors, from industrial automation, quality control, manufacturing, transport, banking and cyber security to health and social care.
By working on real-life case studies with industry tools, you'll become proficient in embedded systems tools and techniques for machine learning on the edge applications for industry and apply your hardware and software skills in a major project utilizing advanced machine learning techniques.
In addition to the tuition fees, you will also need to purchase some hardware, such as ST DISCO-L475E, and sensors, which we do not expect to exceed £100. You will also need access to a fairly modern laptop or personal computer which runs Microsoft Windows 10 or later. Furthermore, admin rights are required to install relevant software packages.
You may also be interested in our online Embedded Computing on Arm PG Cert, or these modules may be taken on a module-by-module basis. Contact us for further details.
Master of Science - MSc (PG)
| Level | RQF Level 7 |
|---|---|
| Entry requirements | Applicants will normally hold a first or second class first degree. While a prior degree in a subject containing computing or electronics is welcome, the course is open to applicants with an electronics / computing background and a passion for technology whose first degrees may be in other subjects. A Foundation degree in computing or electronics with an appropriate period of industrial experience may also be considered. Each applicant for the Master’s programme who possesses a Foundation degree will be expected to attend an interview where an assessment will be made to determine the standard of their industrial experience and suitability for the course. |
| Level | RQF Level 7 |
|---|---|
| Entry requirements | Applicants will normally hold a first or second class first degree. While a prior degree in a subject containing computing or electronics is welcome, the course is open to applicants with an electronics / computing background and a passion for technology whose first degrees may be in other subjects. A Foundation degree in computing or electronics with an appropriate period of industrial experience may also be considered. Each applicant for the Master’s programme who possesses a Foundation degree will be expected to attend an interview where an assessment will be made to determine the standard of their industrial experience and suitability for the course. |
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