MSc AI
About
The MSc Artificial Intelligence is a specialist programme that has the core aim of preparing you for a 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. You will also be able to apply the acquired skills in the development of AI system and applications.
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 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.
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. We particularly welcome female applicants, as they are under-represented within the School at postgraduate taught courses.
Start Date
September 2025 (Full-Time and Part-Time)
January 2026 (Full-Time and Part-Time)
Study Mode and Duration
Full-Time: Three semesters (12 months study)
Part-Time: Six semesters over 3 academic years
Campus
Belfast
Teaching, learning and Assessment
Teaching is delivered through lectures, directed tutorials, seminars, and practical sessions, some of which are by industry professionals / researchers.
Masters project is conducted with a dedicated supervisor for each student.
The course is assessed by 100% coursework
Attendance
Each module is timetabled 5 hours per week, typically starting mid-afternoon on a weekday including lectures, tutorials and practicals for the taught components of the course. Research Project takes place in the final semesters separately. Student can select 1 or 2 taught module(s) to study per semester.
Duration
6 semesters over 3 Academic years (2.5 years)
Career options
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.
Contact
Dr. Shuai Zhang - Course Director
Modules
The MSc award consists of six taught modules, in addition to a substantial piece of independent Masters Project. The taught modules in the course are listed below:
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.
Entry 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.
Eligibility
Places are limited and open to applicants who:
- are over 18 years of age;
- are eligible to work in Northern Ireland;
- are ‘settled’ in Northern Ireland, and has been ordinarily resident in the UK for at least three years; or
- are a person who has indefinite leave to enter or remain in the UK.
- meet the course specific entry requirements. See course pages for requirements.
- meet the Ulster University general entry requirements