We are passionate about sharing with our students the vital role they each have now and as future professionals in promoting a sustainable future for all. We believe that sustainability is not the domain of one discipline or profession. It is the responsibility of all disciplines, professions, organisations and individuals.
That is why on each of our courses within the School of Computing you will learn about the UN Sustainable Development Goals and the contribution you can make now, and as a graduate in Computing.
Read the course details below to find out more.
Creating the next generation of high quality professionals for the AI industry.
The MSc Artificial Intelligence is a specialist programme that has the core aim of preparing students for both an industrial career, with skills in the fields of computing, knowledge presentation, reasoning, robotics, machine learning, deep learning, neural networks, natural language processing, data analytics in the context of AI and the emerging and advanced topics in AI, and in addition providing a relevant platform to embark on further research studies. They will also be able to apply the acquired skills in the development of AI system and applications.
The delivery of the course is supported by multi-million pound infrastructure of a large-scale pervasive and mobile computing environment, a suite of contemporary sensing technologies and rapid prototyping facilities. The course content has been informed by internationally leading research being conducted in the School and by our strong industry partnerships, most notably with BT through the BT Ireland Innovation Centre (BTIIC) and with PwC through the Advanced Research and Engineering Centre (ARC).
This course has been developed in response to evidence of demand from industry and business for up-skilling of staff in the area of AI and addresses a clear gap in the marketplace for postgraduate study. The MSc in Artificial Intelligence will strive to address the growing demands in the sector by training computing professionals to follow a career in the AI industry. The course also provides a platform to embark on further research studies.
The course is accredited by BCS, The Chartered Institute for IT, for Partial CITP (Chartered IT Professional) and Partial CEng (Chartered Engineer).
The School of Computing at Ulster University holds Athena Swan Bronze Award since 2016 and is committed to promote and advance gender equality in Higher Education. W particularly welcome female applicants, as they are under-represented within the School at postgraduate taught courses.
We know that choosing to study at university is a big decision, and you may not always be able to find the information you need online.
Please contact Ulster University with any queries or questions you might have about:
For any queries regarding getting help with your application, please select Admissions in the drop down below.
For queries related to course content, including modules and placements, please select Course specific information.
We look forward to hearing from you.
In this section
The MSc award consists of six compulsory taught modules (totaling 120 credits), in addition to a substantial piece of independent Masters Project (60 credits).
Data Science and Machine Learning
This module provides an overview of Data Science process/pipeline. It provides systematic understanding of mathematical and statistical knowledge for explorable data analysis (EDA) and to understand the foundations of supervised and unsupervised machine learning algorithms, and with the practical programming skills to apply them to real world datasets. The module discusses the constraints that needs to be considered when designing, implementing, evaluating and visualising solutions to real-world complex problems.
Deep Learning and Its Application
The module will introduce the fundamentals of deep learning, construction of neural networks and theory of developing successful deep learning algorithms. Students will learn state of the art convolutional neural networks, recurrent neural networks, loss functions and optimisation process along with development tools, and apply them to develop solutions for applications of computer vision and natural language processing.
Robotics & AI
This module provides an overview of smart robotics and AI. It is designed to provide students with a strong foundation through the core topics and the key technologies of robotics and AI while providing hands-on experience on programming smart robots in the labs. The module will explore practically coding AI techniques for Robotics and the focus is given to design and implement smart robots exhibiting AI behaviours.
Knowledge Engineering
This module will cover modern topics in a classical field of artificial intelligence, including knowledge representation and reasoning (deductive and inductive), and their effective utilisation in e.g. decision making, automated reasoning and formal verification, and semantic web. Students will gain deep understanding of key concepts and principles, and gain practical skills in critically evaluating and effectively building knowledge-based applications.
Intelligence Engineering and Infrastructure
The aim of this module is to educate students on best practices for engineering, deploying, testing and orchestration intelligence across modern computing. This will include aspects of Machine learning, federated operation of activities, data engineering, production of tailored computational artefacts (such as models which are tailored for a range of device type), production pipelines, automated testing and automated deployment.
Emerging and Advanced Topics in AI
This module will cover cutting-edge topics in the field of artificial intelligence, including recent advances in AI theory, algorithms and applications, as well as issues such as privacy, fairness and ethics in artificial intelligence. In doing so a number of examples of advanced AI systems and applications are reviewed. Students will gain deep understanding of key concepts, principles, and challenges, and gain practical skills in critically evaluating and effectively building AI-based applications. The module will also help students develop their skills in independent learning, research skills, writing, as well as practical skills in using software to reproduce results from the literature.
Typically 15 timetabled hours per week Monday – Friday including lectures, tutorials and practicals in the computer labs for the taught components of the course. Research Project takes place in the final semester seperately.
Teaching is delivered through lectures, directed tutorials, seminars, and practical sessions, some of which are by industry professionals / researchers.
The course is assessed by 100% coursework.
The content for each course is summarised on the relevant course page, along with an overview of the modules that make up the course.
Each course is approved by the University and meets the expectations of:
As part of your course induction, you will be provided with details of the organisation and management of the course, including attendance and assessment requirements - usually in the form of a timetable. For full-time courses, the precise timetable for each semester is not confirmed until close to the start date and may be subject to some change in the early weeks as all courses settle into their planned patterns. For part-time courses which require attendance on particular days and times, an expectation of the days and periods of attendance will be included in the letter of offer. A course handbook is also made available.
Courses comprise modules for which the notional effort involved is indicated by its credit rating. Each credit point represents 10 hours of student effort. Undergraduate courses typically contain 10, 20, or 40 credit modules (more usually 20) and postgraduate courses typically 15 or 30 credit modules.
The normal study load expectation for an undergraduate full-time course of study in the standard academic year is 120 credit points. This amounts to around 36-42 hours of expected teaching and learning per week, inclusive of attendance requirements for lectures, seminars, tutorials, practical work, fieldwork or other scheduled classes, private study, and assessment. Teaching and learning activities will be in-person and/or online depending on the nature of the course. Part-time study load is the same as full-time pro-rata, with each credit point representing 10 hours of student effort.
Postgraduate Master’s courses typically comprise 180 credits, taken in three semesters when studied full-time. A Postgraduate Certificate (PGCert) comprises 60 credits and can usually be completed on a part-time basis in one year. A 120-credit Postgraduate Diploma (PGDip) can usually be completed on a part-time basis in two years.
Class contact times vary by course and type of module. Typically, for a module predominantly delivered through lectures you can expect at least 3 contact hours per week (lectures/seminars/tutorials). Laboratory classes often require a greater intensity of attendance in blocks. Some modules may combine lecture and laboratory. The precise model will depend on the course you apply for and may be subject to change from year to year for quality or enhancement reasons. Prospective students will be consulted about any significant changes.
Assessment methods vary and are defined explicitly in each module. Assessment can be a combination of examination and coursework but may also be only one of these methods. Assessment is designed to assess your achievement of the module’s stated learning outcomes. You can expect to receive timely feedback on all coursework assessments. This feedback may be issued individually and/or issued to the group and you will be encouraged to act on this feedback for your own development.
Coursework can take many forms, for example: essay, report, seminar paper, test, presentation, dissertation, design, artefacts, portfolio, journal, group work. The precise form and combination of assessment will depend on the course you apply for and the module. Details will be made available in advance through induction, the course handbook, the module specification, the assessment timetable and the assessment brief. The details are subject to change from year to year for quality or enhancement reasons. You will be consulted about any significant changes.
Normally, a module will have 4 learning outcomes, and no more than 2 items of assessment. An item of assessment can comprise more than one task. The notional workload and the equivalence across types of assessment is standardised. The module pass mark for undergraduate courses is 40%. The module pass mark for postgraduate courses is 50%.
The class of Honours awarded in Bachelor’s degrees is usually determined by calculation of an aggregate mark based on performance across the modules at Levels 5 and 6, (which correspond to the second and third year of full-time attendance).
Level 6 modules contribute 70% of the aggregate mark and Level 5 contributes 30% to the calculation of the class of the award. Classification of integrated Master’s degrees with Honours include a Level 7 component. The calculation in this case is: 50% Level 7, 30% Level 6, 20% Level 5. At least half the Level 5 modules must be studied at the University for Level 5 to be included in the calculation of the class.
All other qualifications have an overall grade determined by results in modules from the final level of study.
In Masters degrees of more than 200 credit points the final 120 points usually determine the overall grading.
Figures from the academic year 2022-2023.
Academic staff in the School of Computing are qualified to teach in higher education with most of them holding at least a Postgraduate Certificate in Higher Education Practice. The majority of academic staff in the School (89%) are accredited fellows of the Higher Education Academy (HEA) or above. Within the School of Computing courses are taught by staff who are Professors (22%), Readers/Senior Lecturers (28%) and Lecturers (50%)
The University employs over 1,000 suitably qualified and experienced academic staff - 60% have PhDs in their subject field and many have professional body recognition.
Courses are taught by staff who are Professors (19%), Readers, Senior Lecturers (22%) or Lecturers (57%).
We require most academic staff to be qualified to teach in higher education: 82% hold either Postgraduate Certificates in Higher Education Practice or higher. Most academic and learning support staff (85%) are recognised as fellows of the Higher Education Academy (HEA) by Advance HE - the university sector professional body for teaching and learning. Many academic and technical staff hold other professional body designations related to their subject or scholarly practice.
The profiles of many academic staff can be found on the University’s departmental websites and give a detailed insight into the range of staffing and expertise. The precise staffing for a course will depend on the department(s) involved and the availability and management of staff. This is subject to change annually and is confirmed in the timetable issued at the start of the course.
Occasionally, teaching may be supplemented by suitably qualified part-time staff (usually qualified researchers) and specialist guest lecturers. In these cases, all staff are inducted, mostly through our staff development programme ‘First Steps to Teaching’. In some cases, usually for provision in one of our out-centres, Recognised University Teachers are involved, supported by the University in suitable professional development for teaching.
Figures from the academic year 2022-2023.
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We recognise a range of qualifications for admission to our courses. In addition to the specific entry conditions for this course you must also meet the University’s General Entrance Requirements.
Applicants must:
(a) have gained
(i) a second class honours degree or better, in the subject areas of Computer Science, Software Engineering, Electronic Engineering, Electrical Engineering, Mathematics, Physics or closely related discipline, from a university of the United Kingdom or the Republic of Ireland, or from a recognised national awarding body, or from an institution of another country which has been recognised as being of an equivalent standard; or
(ii) an equivalent standard (normally 50%) in a Graduate Diploma, Graduate Certificate, Postgraduate Certificate or Postgraduate Diploma or an approved alternative qualification excluding Conversion courses; and the qualification must be in the subject areas of Computer Science, Software Engineering, Electronic Engineering, Electrical Engineering, Mathematics, Physics or closely related discipline.
and
(b) provide evidence of competence in written and spoken English (GCSE grade C or equivalent). For applicants whose first language is not English the minimum English language requirement is an Academic IELTS 6.0 with no band score less than 5.5, Trinity ISE: Pass at level III or equivalent English language tests comparable to IELTS equivalent score.
In exceptional circumstances, as an alternative to (a) (i) or (a) (ii) and/or (b), where an individual has substantial and significant experiential learning, a portfolio of written evidence demonstrating the meeting of graduate qualities (including subject-specific outcomes, as determined by the Course Committee) may be considered as an alternative entrance route. Evidence used to demonstrate graduate qualities may not be used for exemption against modules within the programme.
English language requirements for international applicants
The minimum requirement for this course is Academic IELTS 6.0 with no band score less than 5.5. Trinity ISE: Pass at level III also meets this requirement for Tier 4 visa purposes.
Ulster recognises a number of other English language tests and comparable IELTS equivalent scores.
The entry requirements facilitate accreditation of prior learning.
In this section
AI is at the centre of Industry 4.0 – The Fourth Industrial Revolution. Many countries, including the UK, US and China, have taken AI as a priority area for research and development. The UK government and industry have committed grow UK's AI capacity. The MSc in Artificial Intelligence will strive to address the growing demands being placed on the sector by training computing professionals to follow a career either in industry or academia. The course provides opportunities for training and development of the skills required to contribute to the local and global industrial opportunities that AI offers. AI has applications in almost every industry sector including, but not limited to, Health, Financial Technology, Advanced Manufacturing, Media, Energy, Civic Society and Public Policy. Graduates from the course will be well placed to progress into a career across a range of industrial settings in these sectors. The School have active Industry engagement and links with vibrant technology sector in Northern Ireland. Graduates from the course also have opportunity to embark on further research at the Ph.D. level.
There is no placement as part of the course, however, there are opportunities in the course for you to participate in research and industry related projects through our two Innovation centres BTIIC, CHIC and ARC.
BTIIC is the BT Ireland Innovation Centre (BTIIC) in collaboration with Ulster University and BT. The centre aims to invent new ways of using data analytics, artificial intelligence and the IoT, through two work streams of Intelligent System and IoT.
CHIC is the Connected Health Innovation Centre is funded by Invest NI to support business led research in the area of connected health, with focus on data analytics and IoT. The centre currently has over 30 national and international member companies with both technical expertise and clinical experience.
Advanced Research Centre (ARC) is a joint centre with PWC, Ulster and Queens University Belfast. ARC brings together researchers, engineers and business executives, combining expertise from academia and industry within one R&D centre and aims to downstream innovative research into commercial applications. Three technical work streams are established at Ulster on Digital Transparency, Digital Transformation and Digital Empowerment.
£7,000.00
£17,090.00
It is important to remember that costs associated with accommodation, travel (including car parking charges) and normal living will need to be covered in addition to tuition fees.
Where a course has additional mandatory expenses (in addition to tuition fees) we make every effort to highlight them above. We aim to provide students with the learning materials needed to support their studies. Our libraries are a valuable resource with an extensive collection of books and journals, as well as first-class facilities and IT equipment. Computer suites and free Wi-Fi are also available on each of the campuses.
There are additional fees for graduation ceremonies, examination resits and library fines.
Students choosing a period of paid work placement or study abroad as a part of their course should be aware that there may be additional travel and living costs, as well as tuition fees.
See the tuition fees on our student guide for most up to date costs.
We know that choosing to study at university is a big decision, and you may not always be able to find the information you need online.
Please contact Ulster University with any queries or questions you might have about:
For any queries regarding getting help with your application, please select Admissions in the drop down below.
For queries related to course content, including modules and placements, please select Course specific information.
We look forward to hearing from you.
Ulster continues to develop and support sustainability initiatives with our staff, students, and external partners across various aspects of teaching, research, professional services operations, and governance.
At Ulster every person, course, research project, and professional service area on every campus either does or can contribute in some way towards the global sustainability and climate change agenda.
We are guided by both our University Strategy People, Place and Partnerships: Delivering Sustainable Futures for All and the UN Sustainable Development Goals.
Our work in this area is already being recognised globally. Most recently by the 2024 Times Higher Education Impact rating where we were recognised as Joint 5th Globally for Outreach Activities and Joint Top 20 Globally for Sustainable Development Goal 17: Partnership for the Goals.
Visit our Sustainability at Ulster destination to learn more about how the University strategy and the activities of Ulster University support each of the Sustainable Development Goals.
