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MSc|Postgraduate Taught

Artificial Intelligence

Entry year
Entry requirements
1 year (Full-time)
3 years (Part-time)
Places available
TBC (Full Time)
TBC (Part Time)

In the last decade the advances in Artificial Intelligence have made it at the forefront of technology, with many advances improving our daily lives.

Such is its importance that AI has become a national priority in many countries, including the UK, US, China, and India.

As a result, there is a huge demand for specialist graduates with advanced AI knowledge and skills.

Studying MSc Artificial Intelligence at Queen’s provides you with the building blocks required for a career in the AI sector, as a researcher or an engineer.

Course Structure

Through a combination of lectures, tutorials, and practical learning you will investigate the fundamentals of AI and the latest AI technologies. By familiarising yourself with the main areas of AI that are already being used in industry you will be primed to push this learning even further. With each module acting as a building block that allows you to work towards a themed research project.

An analytical, curious, technical, and ambitious individual. You are ready to expand the horizons of what is possible.

You will appreciate the growing demand for AI in the world and would seek to use these skills to further your career in this exciting and expanding area.

Ideally, you will be a Computing graduate with strong programming skills and a solid background in mathematics.


Artificial Intelligence highlights

Industry Links

Developed in direct response to industry need this course will provide the building blocks required for you to step into a career in AI.

World Class Facilities

Most of the lectures and lab based activities are in our Computer Science Building opened in 2016 after a £14 million re-development. The four-storey, 3,000m2, state-of-the-art facility has large well-equipped computing labs, including a dedicated AI Lab, and formal and informal student spaces which support a high level of group and project work.

Internationally Renowned Experts

The teaching team are specialists in each subject area and bring a wealth of up-to-date knowledge to the course. They have extensive research experience in their subject area and are noted for their research output.

Student Experience

The programme development team have experience in AI programme design at MSc level. This programme is newly designed to minimise module overlap, maximise employment relevancy and content recency; to consider knowledge/skill longevity and between-year continuity; to be free of legacy issues (existing course provision, staff). Four new AI staff members are recruited to best match the new design.

Industry Links


BT, BBC, PwC, Kainos, Datactics,
Microsoft, Google, Facebook, Oosto (formerly Anyvision), etc

Career Development


A thought leader in AI, showcasing technological advancements through research. Working for some of the largest companies on the planet. Or even advising government policy. The future is an exciting place, full of opportunity.

Course Structure

Students may enrol on a full-time (1 year) or part-time (3 years) basis.

Normally, taught modules are delivered using “block mode” delivery. In block mode delivery, modules are delivered in sequence, with each individual module being taught in a 4-week period. This consists of face-to-face teaching (lectures/labs) timetabled between 9am and 5pm for two and half days per week, over three weeks. The fourth week in the module block is typically reserved for coursework and independent study.

We provide flexibility for self-directed learning by making teaching materials available online but it is typically expected that both full-time and part time / PG Cert students are generally available for the timetabled contact hours.

Full-time students typically complete three modules per semester. Part-time students typically complete one or two modules per semester.

The MSc is awarded to students who successfully complete six taught modules (120 CATS points) and a 15,000 - 20,000 word research dissertation (60 CATS points ).

Other options available are:
- Postgraduate Diploma (by successfully completing 120 CATS points from taught modules)
- Postgraduate Certificate (by successfully completing 60 CATS points from taught modules).

AI for Health

This module will serve as a case study of AI applications. It will cover contemporary digital health topics such as precision medicine, diagnostics, medical imaging and drug discovery. It will develop the ability to utilise AI principles and techniques to solve some health challenges, the ability to obtain relevant data from recognised repositories, the ability to utilise existing libraries and packages for analysing and visualising health data, and the transferable skills to apply AI to solve practical challenges.

Computer Vision

This module will cover deep neural networks (DNNs) and modern approaches to computer vision including DNN models for various computer vision tasks and current topics of computer vision. It will develop the ability to utilise DNN models to solve real-world computer vision challenges, the ability to obtain image/video data from recognised repositories, the ability to utilise existing libraries and packages for implementing appropriate DNN models for a given computer vision task.

Course Details

The MSc in Artificial Intelligence is available in a full-time or a part-time option.

Full-time (1-year)

Part-time (2+ years): Part-time students are normally enrolled for two years.

Modules are regularly updated to reflect new developments in the dynamic field of Artificial Intelligence. Modules offered may be subject to change.

Foundations of AI

This module will cover the fundamental mathematics underlying AI including probability and statistics, calculus, algebra and optimisation. It will provide you with a sound understanding of the fundamentals; develop the ability to utilise them to understand and explain various AI techniques, and the ability to identify the most suitable modelling, optimisation, factorisation, and transformation approach for a given problem.

Knowledge Engineering

This module will cover classical and modern knowledge engineering techniques including logic, ontology, knowledge graph, and uncertainty reasoning. It will provide you with a systematic understanding of knowledge, principles and procedures of knowledge engineering, develop your ability to utilise suitable knowledge-based methods to solve real-world problems, and ability to evaluate and compare the performance of knowledge-based solutions for a given problem.

Machine Learning

This module will cover different types of machine learning and various algorithms of each type. It will provide you with a systematic understanding of machine learning as a subject area, develop your ability to identify problems that can be solved using machine learning methods, to apply suitable machine learning algorithms and software packages to solve real-world problems, to evaluate and compare the performance of machine learning methods for a given problem, and to present and discuss the results of machine learning methods and propose appropriate improvements to methods.

Natural Language Processing

This module will mainly cover modern approaches to natural language processing (NLP), including various deep neural networks (DNNs) for NLP, current topics of NLP.
It will develop the ability to utilise DNN models to solve real-world NLP challenges, the ability to obtain text/speech data from recognised repositories, the ability to utilise existing libraries and packages for developing NLP models, and an awareness of current developments, methods and applications of NLP.

Themed Project

A themed project is a research project in an approved theme. Each theme may run for a number of years, which is related to a strong area of research in the School. The topic of each project should be drawn from the following thematic areas of artificial intelligence (AI) covered by the Programme: machine learning (e.g., detection learning), knowledge engineering (e.g., clinical decision support systems, AI for education), computer vision (e.g., video search), natural language processing (e.g., question answering), and AI for health (e.g., medical image processing, biomarker discovery). Subject to approval by the Programme Committee, other themes not covered by the Programme may be included. These additional themes may be sponsored by a third party (e.g., a company) and sponsorship may be in the form of paying for the Levelling-Up Programme at the start of the Project. In exceptional cases, a project may have a topic outside these thematic areas.

People teaching you

Professor of Artificial Intelligence

School of EEECS

Learning and Teaching

Learning opportunities associated with this course are outlined below:

  • Academic Team

    You will be taught by a teaching team who are specialists in each subject area and bring a wealth of up-to-date knowledge to the course. This extensive research experience combined with group projects in small teams offers you the perfect environment to study AI.

  • English Language Support

    The school is offering support on the use of English in academic writing. This will help you not only during your studies at Queen’s, but also in your future career.

  • Modules

    Each of the six taught modules in the Course is designed to help you incrementally build your knowledge, understanding, and skills of AI. Starting with learning core AI principles with Foundations of AI, Machine Learning, and Knowledge Engineering, the course then progresses to focus on Computer Vision and Natural Language Processing. The final taught module will expose you to real-world applications of AI with our AI for Health module, an area of world-renowned research excellence at Queen’s University, allowing you to put theory into practice in an applied setting.

    The taught modules will also prepare you for a final, large-scale research project which will provide you with an opportunity to showcase your knowledge and skills in a thematic area.

  • Transferrable Skills

    This course is designed to deliver qualified and sought-after graduates ready for the future of AI technology.

  • Virtual Learning Environment

    All modules have a virtual learning environment (using Canvas) where the students can find all relevant material (lecture notes, handouts, video lectures) as well as online quizzes and assignments. Without a doubt, having all learning resources in one place is very useful.


Assessments associated with the course are outlined below:

  • Awarding of the qualifications is based on continuous assessment of coursework and assessment of modules is based solely on submitted work related to private, individual study .

    A Postgraduate Diploma student who achieves 50 per cent or higher in coursework may be permitted to transfer to the MSc subject to General Regulations. The MSc will be awarded with Distinction to a student who achieves a dissertation and average mark both exceeding 70 per cent.




The information below is intended as an example only, featuring module details for the current year of study (2023/24). Modules are reviewed on an annual basis and may be subject to future changes – revised details will be published through Programme Specifications ahead of each academic year.

  • Year 1

    Core Modules

    Computer Vision (20 credits)
    Machine Learning (20 credits)
    Foundations of AI (20 credits)

Entrance requirements


Normally a 2.1 Honours degree or equivalent qualification acceptable to the University in Computer Science, Software Engineering, Electrical and/or Electronic Engineering, Mathematics with Computer Science, Physics with Computer Science or a related discipline. Applicants must normally have achieved 2:1 standard or above in relevant modules.

Applicants who hold a 2.2 Honours degree and a Master’s degree (or equivalent qualifications acceptable to the University) in one of the above disciplines will be considered on a case-by-case basis.

All applicants will be expected to have mathematical ability and significant programming experience as evidenced either through the content of their primary degree or through another appropriate formal qualification.

Applications may be considered from those who do not meet the above requirements but can provide evidence of recent relevant technical experience in industry, for example, in programming.

The University's Recognition of Prior Learning Policy provides guidance on the assessment of experiential learning (RPEL). Please visit for more information.

The deadline for applications is normally 16th August 2024.  However, we encourage applicants to apply as early as possible.  In the event that any programme receives a high number of applications, the University reserves the right to close the application portal earlier than the deadline.  Notifications to this effect will appear on the Direct Application Portal against the programme application page.

Please note: For International / EU Other / ROI / GB applicants a deposit will be required to secure a place on this course.

AICC funding: A limited number of fully funded places (provided by the Department for the Economy) are available for this programme for eligible applicants resident in Northern Ireland. Where there are more eligible applicants than places available the academic selectors will make offers in rank order based on academic merit and potential as evidenced in the totality of the information provided within each application. We will operate a waiting list as required to allow us to fill all available places. You will be notified as soon as possible after the deadline whether your application has been selected for a funded place. If you have not been selected for a funded place, we will accept self-funded or employer-funded applicants, if spaces are available. More details can be found at the link below. Application deadline for AICC funding is Friday 14th June at 12 noon.

International Students

Our country/region pages include information on entry requirements, tuition fees, scholarships, student profiles, upcoming events and contacts for your country/region. Use the dropdown list below for specific information for your country/region.

English Language Requirements

Evidence of an IELTS* score of 6.0, with not less than 5.5 in any component, or an equivalent qualification acceptable to the University is required. *Taken within the last 2 years

International students wishing to apply to Queen's University Belfast (and for whom English is not their first language), must be able to demonstrate their proficiency in English in order to benefit fully from their course of study or research. Non-EEA nationals must also satisfy UK Visas and Immigration (UKVI) immigration requirements for English language for visa purposes.

For more information on English Language requirements for EEA and non-EEA nationals see:

If you need to improve your English language skills before you enter this degree programme, INTO Queen's University Belfast offers a range of English language courses. These intensive and flexible courses are designed to improve your English ability for admission to this degree.

  • Academic English: an intensive English language and study skills course for successful university study at degree level
  • Pre-sessional English: a short intensive academic English course for students starting a degree programme at Queen's University Belfast and who need to improve their English.



Career Prospects



Prizes and Awards

Teachers working on classroom-based dissertation projects may apply for the Northern Ireland Centre for Educational Research (NICER) award .

Graduate Plus/Future Ready Award for extra-curricular skills

In addition to your degree programme, at Queen's you can have the opportunity to gain wider life, academic and employability skills. For example, placements, voluntary work, clubs, societies, sports and lots more. So not only do you graduate with a degree recognised from a world leading university, you'll have practical national and international experience plus a wider exposure to life overall. We call this Graduate Plus/Future Ready Award. It's what makes studying at Queen's University Belfast special.

Tuition Fees

Northern Ireland (NI) 1 £7,300
Republic of Ireland (ROI) 2 £7,300
England, Scotland or Wales (GB) 1 £9,250
EU Other 3 £25,800
International £25,800

1EU citizens in the EU Settlement Scheme, with settled status, will be charged the NI or GB tuition fee based on where they are ordinarily resident. Students who are ROI nationals resident in GB will be charged the GB fee.

2 EU students who are ROI nationals resident in ROI are eligible for NI tuition fees.

3 EU Other students (excludes Republic of Ireland nationals living in GB, NI or ROI) are charged tuition fees in line with international fees.

All tuition fees quoted relate to a single year of study unless stated otherwise. Tuition fees will be subject to an annual inflationary increase, unless explicitly stated otherwise.

More information on postgraduate tuition fees.

Additional course costs

Students may incur additional costs for small items of clothing and/or equipment necessary for lab or field work.

All Students

Depending on the programme of study, there may be extra costs which are not covered by tuition fees, which students will need to consider when planning their studies.

Students can borrow books and access online learning resources from any Queen's library. If students wish to purchase recommended texts, rather than borrow them from the University Library, prices per text can range from £30 to £100. Students should also budget between £30 to £75 per year for photocopying, memory sticks and printing charges.

Students undertaking a period of work placement or study abroad, as either a compulsory or optional part of their programme, should be aware that they will have to fund additional travel and living costs.

If a programme includes a major project or dissertation, there may be costs associated with transport, accommodation and/or materials. The amount will depend on the project chosen. There may also be additional costs for printing and binding.

Students may wish to consider purchasing an electronic device; costs will vary depending on the specification of the model chosen.

There are also additional charges for graduation ceremonies, examination resits and library fines.

How do I fund my study?

The Department for the Economy will provide a tuition fee loan of up to £6,500 per NI / EU student for postgraduate study. Tuition fee loan information.

A postgraduate loans system in the UK offers government-backed student loans of up to £11,836 for taught and research Masters courses in all subject areas. Criteria, eligibility, repayment and application information are available on the UK government website.

More information on funding options and financial assistance - please check this link regularly, even after you have submitted an application, as new scholarships may become available to you.

International Scholarships

Information on scholarships for international students, is available at



How to Apply

Apply using our online Postgraduate Applications Portal and follow the step-by-step instructions on how to apply.

Apply now

When to Apply

The deadline for applications is normally 30th June 2021. In the event that any programme receives a high volume of applications, the university reserves the right to close the application portal earlier than 30th June deadline. Notifications to this effect will appear on the Direct Entry Portal (DAP) against the programme application page.

Terms and Conditions

The terms and conditions that apply when you accept an offer of a place at the University on a taught programme of study.
Queen's University Belfast Terms and Conditions.

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