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Artificial Intelligence MSc (Distance Learning)

2025-26 (also available for 2024-25)

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This course is eligible for Master's loan funding. Find out more.

Start date

22 September 2025

17 November 2025

16 February 2026

18 May 2026

Duration

2-3 years part-time

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About the course

Reasons to study

  1. Professional Links – This course is accredited by the British Computer Society (BCS), the Chartered Institute for the IT industry. This gives you a potential advantage when looking for a job as some employers may ask for graduates with accredited degrees.
  2. Latest knowledge - You will be taught by academic staff who have world-leading expertise in research that applies AI methods to solve key societal challenges, meaning you'll develop knowledge and skills that are current and highly relevant to industry.
  3. Convenience – This course is delivered as a Distance Learning programme, with access to materials readily available online. If you wish to be eligible for a Government student loan you can even undertake this course part-time over a duration of two years.

Jumpstart a rewarding career in Artificial Intelligence (AI) with our AI MSc Distance Learning course at The University of Huddersfield. Aimed at people who aren’t local to our campus or who are in employment, this Artificial Intelligence MSc will equip you with the knowledge and skills to apply appropriate solutions to real-world problems using a range of AI technologies.

Our Artificial Intelligence course seeks to deepen your understanding of a range of areas, including machine learning, data mining and robotics.

Why study Artificial Intelligence MSc (Distance Learning) at Huddersfield?

In the UK, we have seen a huge expansion in artificial intelligence (AI) during the last decade, showcasing that the country’s economy is aiming to use intelligent technologies to position itself at the forefront of the digital revolution. Data shows that the AI sector is now worth over £15.6bn and employs more than 35,000 people.*

Demand for AI talent in this rapidly developing field is therefore high. At The University of Huddersfield, our MSc in Artificial Intelligence will equip you with the skills needed to contribute to the UK AI sector. This course aims to develop your knowledge and understanding to an advanced level across a range of areas, including:

  • Machine learning
  • Data Mining
  • Robotics
  • Knowledge graphs
  • Autonomous Systems

Research plays an important role in informing all our teaching and learning activities. Many of our academics are members of the University's Centre for Autonomous and Intelligent Systems and are at the forefront of impactful research. Our research expertise spans the whole spectrum of modern AI, from automated planning and knowledge representation and reasoning, to machine learning, deep learning and generative AI. We apply this expertise to solve key societal challenges related to healthcare, transportation and resilience.

We will equip you with an understanding of the fundamental approaches to implementing intelligent behaviour in machines. This should then enable you to match applications with appropriate AI techniques for their solution. You will also be able to construct and configure solutions using a range of AI technologies.

This course is fully accredited by the British Computer Society (BCS), the Chartered Institute for IT, and by completing it, you will have partially fulfilled the academic requirements for registration as a Chartered IT Professional.

Discover more about Distance Learning at Huddersfield.

*thedatacity.com

Course detail

Machine Learning

Machine Learning techniques are now used widely in a range of applications either stand-alone or integrated with other AI techniques. The Machine Learning module allows you to obtain a fundamental understanding of the subject as a whole: how to embody machines with the ability to learn how to recognise, classify, decide, plan, revise, optimise etc. You will learn which machine learning techniques are appropriate for which learning problem, and what the advantages and disadvantages are for a range of ML techniques. We will consider the widely known data-driven approaches, and specific techniques such as “deep learning”, and investigate the typical applications and potential limitations of these approaches. We will introduce available tools and use them in practical classes, evaluating learning bias and characteristics of training sets. High profile applications of data driven, stand-alone, ML systems will be investigated, such as the AlphaGo method. Where data is sparse, and knowledge is already present in a system, we will investigate methods to improve heuristics of existing AI systems, and to learn or revise domain knowledge. This is essentially the area of model-driven ML, where is often integrated to other reasoning systems.

Data Mining

Data mining is a collection of tools, methods and statistical techniques for exploring and extracting meaningful information from large data sets. It is a rapidly growing field due to the increasing quantity of data gathered by organisations. There is a potential high value in discovering the patterns contained within such data collections. In this module you will look at different data mining techniques and use appropriate data-mining tools in order to evaluate the quality of the discovered knowledge. You will study approaches to preparing data for exploration, supervised and un-supervised approaches to data mining, exploring unstructured data and the social impact of data mining. You will be expected to develop your knowledge such that you are able to contribute to discussions around current application areas and research topics and to increase your background knowledge and understanding of issues and developments associated with data mining.

Robotics

The Robotics module allows you to gain specialist knowledge in robotic devices and autonomous applications by examining the integration of mechanical devices, sensors and ‘intelligent’ computerised robotic agents. You will also explore the latest developments in robotics and intelligent systems through a series of investigative tasks and practical sessions. The module covers essential techniques for the design and development of robotic based systems using a collection of robotic hardware and simulation software. It supports the discussion and analysis of the hardware and software used to build real-world robotic systems. It introduces device and architectural specific topics required to enable students to design and develop software for intelligent autonomous robots. This will include low-level programming of I/O devices for robotic swarms, sensor systems and active modelling and simulation. It will introduce planning for intelligent robots taking a lifecycle approach from theory to activation.

Knowledge Representation and Reasoning

Knowledge representation and reasoning (KR) is the field of artificial intelligence dedicated to representing information about the world in a form that computer systems can manipulate and utilise to solve complex tasks such as making decisions, diagnosing a medical condition, finding suitable answers to queries or having a dialog in a natural language. This module will introduce you to KR principles, languages and algorithms and help you gain experience in using them to solve practical problems. You will also learn about applications such as the semantic web and knowledge graphs which have found deployment in big corporations such as Google and Amazon.

Autonomous and Autonomic Intelligent Systems

Autonomous systems are intelligent systems that can act independently to accomplish goals based on their knowledge and understanding of their environment and the tasks they have to complete. This module aims to cover the background and requirements for intelligent systems autonomy in a wide range of applications, taken from a computer science and software-oriented viewpoint. As well as the technical challenges of system autonomy, you’ll get the opportunity to study ethical and legal issues, and human factors implications.

Case Studies in Data Analytics and Artificial Intelligence

The purpose of this module is to enable you to appreciate the historical, current and future application areas of Artificial Intelligence and Data Analytics in relation to both theoretical and practical aspects and to investigate at least one application area in depth. Case studies discussed in the sessions will provide an exploration of applications in a variety of different areas and will be achieved by combinations of study of current research papers, tutors’ own research and the investigative work of the students within the module.

Effective Research and Professional Practice

This module aims to provide you with skills that are key to helping you become a successful computing researcher or practitioner. You will get the opportunity to study topics including the nature of research, the scientific method, research methods, literature review and referencing. The module aims to cover the structure of research papers and project reports, reviewing research papers, ethical issues (including plagiarism), defining projects, project management, writing project proposals and making presentations.

Artificial Intelligence Planning

This module will recap on the history of automated planning from the days of STRIPS, up to the present day. It will focus on the kinds of assumptions, algorithms, heuristics and representation languages that have been used to create generative planning algorithms. It will illustrate these developments using a range of planning engines and planning platforms. Current application areas and research topics in automated planning, such as hybrid planning, will be discussed and students will be expected to develop their knowledge such that they are able to contribute to such discussions and to increase their background knowledge and understanding of issues and developments associated with AI Planning.

Individual Project

This module enables you to work independently on a project related to a self-selected problem. A key feature in this final stage of the course is that you will be encouraged to undertake an in-company project with an external Client. Where appropriate, however, the Project may be undertaken with an internal Client - research-active staff - on larger research and knowledge transfer projects. The Project is intended to be integrative, a culmination of knowledge, skills, competencies and experiences acquired in other modules, coupled with further development of these assets. In the case where an external client is involved, both the Client and Student will be required to sign a learning agreement that clearly outlines scope, responsibilities and ownership of the project and its products or other deliverables. The Project will be student-driven, with the clear onus on you to negotiate agreement, and communicate effectively, with all parties involved at each stage of the Project.

Entry requirements

Entry requirements for this course are normally:

  • A BSc or BEng Honours degree (2:2 or above) in Computing or Engineering or related subject or an equivalent professional qualification
  • Other qualifications and/or experience that demonstrate appropriate knowledge and skills at an Honours degree level

If your first language is not English, you will need to meet the minimum requirements of an English Language qualification. The minimum for IELTS is 6.0 overall with no element lower than 5.5, or equivalent. Read more about the University’s entry requirements for students outside of the UK on our International Entry Requirements page.

Enhance your career

We would expect to see graduates progress to careers as Robotics Programmer, Machine Learning Engineer, Data Mining Analyst or Software Engineer for example. You could also go on to further study and the University has many options available for postgraduate research which may interest you.*

*Source: LinkedIn
** Percentage of the University’s postgraduate students go on to work and/or further study within fifteen months of graduating. (HESA Graduate Outcomes 2021/22, UK domiciled, other activities excluded).

97%** Graduates employed

Student support

At the University of Huddersfield, you will find support networks and services to help you get ahead in your studies. Our Distance Learning Unit Team are at hand to make your online learning journey a positive, rewarding and successful one.

The Distance Learning Unit has specialist staff who are committed to ensuring that online teaching and learning material is accessible to all. We can recommend and provide training on assistive technologies and software which can support a range of learning styles and additional needs.

During each module, you will be able to rely on your module tutors to provide any academic support you need.

You will always have access to our online learning facilities and should you need any technical support whilst studying, you will find that many of our student support services and resources are available online or accessible during UK working hours.

Find out more about support services including finance, careers, Library, IT and disability and wellbeing.

Important information

Although we always try and ensure we deliver our courses as described, sometimes we may have to make changes for the following reasons

When you enrol as a student of the University, your study and time with us will be governed by our terms and conditions, Handbook of Regulations and associated policies. It is important that you familiarise yourself with these as you will be asked to agree to them when you join us as a student. You will find a guide to the key terms here, along with the Student Protection Plan.

Although we always try and ensure we deliver our courses as described, sometimes we may have to make changes for the following reasons

Changes to a course you have applied for but are not yet enrolled on

If we propose to make a major change to a course that you are holding an offer for, then we will tell you as soon as possible so that you can decide whether to withdraw your application prior to enrolment. We may occasionally have to withdraw a course you have applied for or combine your programme with another programme if we consider this reasonably necessary to ensure a good student experience, for example if there are not enough applicants. Where this is the case we will notify you as soon as reasonably possible and we will discuss with you other suitable courses we can transfer your application to. If you do not wish to transfer to another course with us, you may cancel your application and we will refund you any deposits or fees you have paid to us.

Changes to your course after you enrol as a student

Changes to option modules:

Where your course allows you to choose modules from a range of options, we will review these each year and change them to reflect the expertise of our staff, current trends in research and as a result of student feedback or demand for certain modules. We will always ensure that you have an equivalent range of options to that advertised for the course. We will let you know in good time the options available for you to choose for the following year.

Major changes:

We will only make major changes to non-optional modules on a course if it is necessary for us to do so and provided such changes are reasonable. A major change is a change that substantially changes the outcomes, or a significant part of your course, such as the nature of the award or a substantial change to module content, teaching days (part time provision), type of delivery or assessment of the core curriculum. For example, it may be necessary to make a major change to reflect changes in the law or the requirements of the University’s regulators or a commissioning or accrediting body. We may also make changes to improve the course in response to student, examiners’ or other course evaluators’ feedback or to ensure you are being taught current best practice. Major changes may also be necessary because of circumstances outside our reasonable control, such as a key member of staff leaving the University or being unable to teach, where they have a particular specialism that can’t be adequately covered by other members of staff; or due to damage or interruption to buildings, facilities or equipment, or pandemics.

Major changes would usually be made with effect from the next academic year, but may happen sooner in an emergency. We will notify you as soon as possible should we need to make a major change and will carry out suitable consultation. If you reasonably believe that the proposed change will cause you detriment or hardship we will, if appropriate, work with you to try to reduce the adverse effect on you or find an appropriate solution. Where an appropriate solution cannot be found and you contact us in writing before the change takes effect you can cancel your registration and withdraw from the University without liability to the University for future tuition fees. We will provide reasonable support to assist you with transferring to another university if you wish to do so.

In exceptional circumstances, we may, for reasons outside of our control, be forced to discontinue or suspend your course. Where this is the case, a formal exit strategy will be followed in accordance with the student protection plan.

The Office for Students (OfS) is the principal regulator for the University.

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