Embedded Computing and Machine Learning MSc

Online postgraduate degree

Master your understanding of embedded systems and artificial intelligence and develop the skills to harness the power of machine learning with our flexible online MSc.

  • Supported online learning gives you the flexibility to fit study around your other commitments
  • Learn from experienced and passionate professionals
  • Ideal if you work in or aspire to work in a computing, artificial intelligence or an embedded technology related role
  • Develop your skills and enhance your career prospects
  • Be part of the University of the Year (THE)

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About your online embedded computing course

Our Embedded Computing and Machine Learning MSc is ideal if you’re currently working, or aspire to work, in the fields of computing, technology or AI on the edge and want to advance your understanding and skillset. Our embedded computing course will equip you with the knowledge and skills to excel in your current role and open doors to further your career. The course has been partly developed in partnership with Arm, the global leader in CPU technology, allowing you, in the first two modules, to benefit from industry-relevant educational materials. The course has been designed to study online giving you the flexibility to study, learn and grow professionally, around your other commitments.

Course highlights

  • Advance your career in embedded systems including artificial intelligence (AI) on the edge
  • Develop the skills to harness the power of machine learning applications in the context of industrial electronics
  • Focus on the industry-relevant technologies offered by Arm
  • Benefit from industry expertise
  • Receive support every step of the way from our dedicated distance learning team

How you'll study

This course is studied 100% online
Course length

Study this course over 3 years

Start dates

Start in September

Application deadlineOur next deadline is

31st July 2024

Industry partnership

First two modules developed in partnership with Arm

UK Quality Assured

Course overview

Embedded computing, especially when paired with machine learning, promises to provide the tools and efficiencies 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.

Our MSc in Embedded Computing and Machine Learning is designed to immerse you in the dynamic realm of embedded systems, with a particular focus, in the first two modules, on industry-relevant educational materials developed in partnership with Arm.

The course is suitable for anyone working in embedded computing, AI or machine learning or anyone with an electronics or computing background and a passion for technology. It’s ideal if you’re wanting to develop your knowledge and skills or plan for future career advancement.

You’ll enhance your knowledge and critical understanding of the concepts and theories that underpin embedded systems and data driven techniques. You’ll also develop the skills you need to harness the power of machine learning applications in various industrial contexts with industry expertise, training modules, and real-life case studies supported by Arm.

Our online embedded computing master’s 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. 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.

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.

The major project will provide you with a platform to showcase the acquired skills and knowledge in an application domain of particular interest to you or relevant to your career aspirations.

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 Operating System or more recent. Furthermore, admin rights are required to install relevant software packages.

This course can also be taken as a PG Cert with the option of topping up to a full master's course or these modules may be taken on a module by module basis. Please contact us for further details

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Embedded Systems Essentials with Arm

The ability to understand the fundamental systems in a microcontroller is a key concept in embedded system development. In this module, you’ll learn the fundamental systems that form an embedded system and how to write the code to control relevant components. On completing this module, you’ll develop an understanding of systems such as digital input and output, analogue input and output, Pulse Width Modulation, interrupts and core coding principles.

You’ll develop the theoretical framework and the practical skills necessary to develop simple systems controlled by a microprocessor. This core knowledge will provide you with the necessary skills to go on to develop more complex systems in later modules. You’ll benefit from exposure to a number of real-world examples which will enable you to analyse the requirements of a system and produce a suitable embedded design. Using the provided simulator, you will be able to experiment with various configurations of hardware in a virtual environment and test your code to refine any solution you develop.

IoT and Machine Learning at the Edge on Arm

The Internet of Things (IoT) is a crucial foundation for the next era of computing, and Arm technology is powering the IoT revolution. This module offers beginners a rapid path to acquiring the knowledge necessary to thrive in a world reshaped by IoT advancements. Upon completing this module, you’ll have a solid understanding of the IoT ecosystem, basic practical skills for developing Arm-based IoT applications, and a foundation in the principles and implementation of artificial intelligence (AI), machine learning (ML), and edge ML.

In addition to gaining the fundamentals of the IoT ecosystem and basic practical skills for building Arm-based IoT applications, you’ll also learn about the social implications of this technology. The module also highlights the global impact of IoT applications, particularly in developing economies. From "smart farming" to solar panels and "blood drones," each case study examines IoT in real-world "first mile" and "last mile" applications, helping you grasp the transformative power of this technology in the real world.

The module will also provide a foundation in the basic concepts of Artificial Intelligence and Machine Learning, with a focus on their implementation at the edge server. It will cover the various processes involved in developing an AI pipeline, such as data processing, feature selection, and the training of machine learning algorithms on the edge server. This module will equip you with knowledge of various AI algorithms, including Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN), specifically for applications like computer vision. Additionally, you’ll acquire knowledge about the optimisation processes for edge microcontrollers.

Machine Learning Techniques

Machine learning is a sub-discipline of the Artificial Intelligence that deals with teaching the computer to act without being programmed. In this module, you’ll learn about the tools and algorithms that can be used to create machine learning models. We’ll also look into how large data sets should be divided into a training set and a test set. Different types of problems that can be solved with machine learning will be introduced; supervised learning algorithms and examples of where they can be applied will be presented. A range of parametric algorithms such as linear regression, logistic regression, and non-parametric algorithms such as K-Nearest neighbour, decision trees, SVMs, will be discussed.

To be able to evaluate a model, a few performance metrics will be explored. The metrics chosen have influence on how the performance of machine learning algorithms is measured and compared. An important concept that we must be aware of when training machine learning algorithms is 'overfitting'. You’ll learn to apply regularisation techniques to mitigate overfitting and enhance model performance.

You’ll investigate and experiment with the various models and algorithms covered in the module using standard libraries such as scikit-learn, statsmodels and Python ML packages, gaining practical skills that are directly applicable in real-world scenarios.

Prompt Engineering and Generative AI

The rise of Generative Artificial Intelligence (AI) is undoubtedly one of the biggest technological breakthroughs in modern times.

It is provoking a major rethink in many fundamental aspects of our lives including the balance of work between humans and machines, and the foundational knowledge, skills, and abilities that modern humans ought to learn and develop in the era of Generative AI. With this regard, this course aims to equip you with the crucial skill of prompt engineering and working with Generative AI for efficient task automation, focusing on the art of communicating effectively and systematically with Large Language models to automate tasks and optimise the outcomes. This course begins with a comprehensive introduction to Generative AI, laying the groundwork for an in-depth exploration of prompt engineering. It starts by building a solid understanding of neural networks, deep learning principles, and fundamental Natural Language Processing (NLP) concepts, essential for effectively engaging with AI models. As the course progresses, it delves into Generative AI models, including Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). However, the heart of the curriculum lies in comprehending the ground-breaking capabilities of the Transformer architecture, the foundation of modern Large Language Models. You will develop the theoretical framework and the practical skills necessary to craft creative tailored prompts for Large Language Models and develop automation pipelines, enabling you to exploit these powerful tools for problem-solving and automation across various industries.

Through practical exercises, you’ll explore topics like the design and development of Large Language Models from scratch, working with state-of-the-art Large Language Models, formulating systematic prompts for optimal interaction with Large Language Models, Working with APIs, task automation, end-to-end data pipelining, as well as content creation. The course also highlights the ethical considerations of using Large Language Models, emphasizing the need for responsible prompt engineering to mitigate biases and maintain fairness in AI applications.

Postgraduate Major Project (Distance Learning)

This module supports students in the preparation and submission of a Masters Stage Dissertation or Project worth 60 credits.

The topic will be based around a project relevant to your specialism and experience. It will be completed solely by yourself as an individual project and, if part of a larger project, the aspect implemented must be clearly identifiable as your sole work. Group projects are not permitted.

The project outline will be formulated and agreed after discussion between yourself and a supervisor at ARU and must meet the objectives of an ARU postgraduate major project.

During the implementation, you will be expected to identify/formulate the problem and issues, conduct literature reviews, evaluate information, investigate and adopt suitable development methodologies, determine solutions, develop hardware/software and/or media artefacts as appropriate, process data, critically appraise and present your findings using a variety of media.

Regular meetings with the project supervisor and workplace mentor should take place, so that the project is closely monitored.

Modules are subject to change and availability.


We'll assess you in several ways including time-constrained assessments, coursework assignments, presentations and a major project. Our dissertation project and module case studies assess your ability to analyse situations, identify key issues, select, synthesise and apply techniques and skills from different modules, and evaluate the appropriateness of solutions when compared to industrial practice.

The dissertation artefact will be based on a real-world scenario.

How you'll study

Our Embedded Computing and Machine Learning course is studied 100% online.

You’ll study through Canvas, our world-class Learning Management System (LMS), which can be accessed from your phone, PC or tablet, both at home or on the move. Canvas provides instant access to study materials, forums, and support from tutors and classmates, as well as enabling easy submission of your assignments. You will also need access to a fairly modern laptop or personal computer which runs Microsoft Windows 10 Operating System or more recent. Furthermore, admin rights are required to install relevant software packages.

There are some scheduled support sessions (2 x 2 hours expected per module) offering useful guidance, should you wish to take advantage of them. You will be notified of the times and dates in advance.

On successful completion of your studies, you’ll be invited to attend a graduation ceremony on campus. If attending the ceremony in person is not possible then we’ll arrange to have your certificate sent to you.

Supported distance learning

We understand that distance learning is different to traditional campus study and if you’re new to online study you may have concerns or apprehensions about studying your MSc Embedded Computing and Machine Learning course, and that’s natural. 

To help put your mind at ease we have a dedicated Distance Learning Support Team to help and support you throughout your time at ARU, starting with your first online induction and staying with you right through to graduation. In addition, you’ll also be supported by specialist tutors who are experienced in supporting distance learning students and will provide you with the support you need throughout your studies. 

Once you start your online machine learning course, we encourage the creation of online communities and many of our learners find these connections with others invaluable, helping them to stay motivated, share concerns or make new friendships. 

Contact us to talk through any questions or concerns or visit our support page for more information about the support services available.

Our support services

Be part of the University of the Year

We're proud to be the Times Higher Education (THE) University of the Year 2023.

The prestigious THE awards honour ’exceptional performance during the 2021-22 academic year, and reflect ARU’s success in delivering high-impact projects during this period, despite the challenges of the Covid-19 pandemic.

The award recognises the difference we make in the region and our communities – while also acknowledging the broader impact of our world-leading research, and the contributions our students and graduates make to society.

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What could an Embedded Computing and Machine Learning MSc do for my career?

The course uniquely combines two popular topics - embedded computing and machine learning – meaning that you can feel confident you are investing your time building skills in specialisms that are sought-after within the industry. It’s ideal for those who wish to advance their existing careers in the technology sector, but also graduates who wish to pursue a master’s degree.

The MSc in Embedded Computing and Machine Learning is strategically designed to immerse students in the dynamic realm of embedded systems, with a particular focus on acquiring industry relevant skills on Arm-based microcontrollers.

The modules within our program not only provide theoretical knowledge but also offer practical exposure, ensuring that graduates are not just proficient in embedded computing concepts but are ready to contribute meaningfully to the industry.

What job roles can I consider with an Embedded Computing and Machine Learning MSc?

Potential job roles that relate to this MSc degree could include:

  • Software engineer
  • Embedded systems engineer
  • Hardware or software architect / developer
  • Applications developer
  • Data architect/engineer

The types of sectors that you could be employed in vary and can include public sector, automotive industry, engineering, manufacturing, telecommunications, and research amongst others.

If you are looking for more information about these job profiles and potential salary earnings see Prospects.ac.uk. It’s important to note that salaries can vary widely depending on the job role, employer, industry sector and location.

What skills will I get from an Embedded Computing and Machine Learning MSc course to help my career?

Choosing to study an Embedded Computing and Machine Learning MSc degree will give you many transferable skills. Here are just some of them:

  • Programming – understanding how to develop and employ machine learning and AI techniques.
  • Enhance your understanding of hardware devices and the relationship between hardware and software.
  • Develop the ability to source, organise and analyse information from various sources.
  • Understand how to review materials to assess strengths and weaknesses of a subject.
  • Conduct research at master’s level.

What can I study after an Embedded Computing and Machine Learning MSc?

Depending on your long-term goals and interests you may decide to explore a doctorate qualification. Having a master’s degree under your belt is a key entry requirement in opening doors to study at this level.

Careers Advice Service

Once you become an ARU student you will be able to access our Employability service to help you whatever the stage of your career, whether that’s landing your dream job or the next progression step.

We offer:

  • careers advice, including one-to-one online and telephone appointments with our experienced advisers
  • help with your CV, job searches, applications and interview preparation
  • an online portal packed with useful careers resources
  • our Employability Programme, which helps you hone the skills employers say they want in graduates.

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.  
  • If English is not your first language, you will be expected to demonstrate a certificated level of proficiency of at least IELTS 6.5 or equivalent English Language qualification as recognised by ARU. You’ll need at least 5.5 in each of the four skills - listening, speaking, reading and writing. 
  • As a distance learner, you'll also need a suitable computer with internet connection, together with sufficient IT competence to make effective use of our online Learning Management System (LMS) with high-speed internet and email. You will also need access to a fairly modern laptop or personal computer which runs Microsoft Windows 10 Operating System or more recent. Furthermore, admin rights are required to install relevant software packages 

Our published entry requirements are a guide only and our decision will be based on your overall suitability for the course as well as whether you meet the minimum entry requirements. Other equivalent qualifications may be accepted for entry to this course, please contact us for further information. 

Fees, funding & additional costs


The full tuition fee for our Embedded Computing and Machine Learning MSc is £8,200.

The tuition fees you pay each year for the online machine learning course will be £2,734. The course is studied over 3 years.

Accredited Prior Learning may reduce the tuition fees. This will be confirmed once your application has been submitted.

Additional costs

You will be required to purchase some hardware, such as ST DISCO-L475E, and sensors, which we do not expect to exceed £100.


Your course can be fully funded by the Postgraduate Student Loans now available (subject to eligibility).

We offer payment by instalments, so you can spread the cost of studying with us.

For military students: You can use your ELCs towards this course. Anglia Ruskin University is a recognised ELCAS provider (number 1007). Please contact your Learning Centre for details of ELC, eligibility and how to apply.

For more information on how you fund your studies please see our funding page.

What our students say

96% of our alumni surveyed would recommend studying with ARU Distance Learning* 
Dio - ARU Student
The flexibility of the online learning is particularly beneficial for those balancing professional and personal commitments. The support from the teaching staff and the extensive online resources creates a robust learning environment.
Read Mario's story
Beth - ARU Distance Learning Student
To be able to delve into a subject that you are passionate about and to explore different topics within that subject was really motivating and helped inform my practice.
Read Beth's story
Roy - ARU Student
I would recommend ARU in a heartbeat. The material is at the cutting edge, the tutors are at the top of their game, and the support is second to none.
Read Roy's story
Cat - ARU Distance Learning Student
It has broadened my knowledge and changed my approach to problem solving. It has pushed me out of my comfort zone to learn new things and look at issues through a different lens.
Read Cat's story

Next application deadline:

31st July 2024

Discover what you're made of.Apply now before the application deadline.

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*2023 alumni survey with 158 respondents