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.
The PG Cert in Artificial Intelligence (AI) is aimed as a starting point to prepare students to embark on an industrial career or further research studies, with knowledge and skills in AI mathematics, knowledge representation and reasoning, machine learning, computer vision, natural language processing, and data analytics. They will also gain experience in applying AI knowledge and skills to develop AI systems and applications. The PG Cert will introduce core taught material, enabling students to gain a good understanding of the range of topics, and acquired skills associated with the creation, evaluation and deployment of AI systems and applications.
Work towards a Master’s at your own pace with our Flexible part-time pathway.
This Postgraduate Certificate (PGCert) program is designed for those seeking a flexible, part-time route to achieving a full Master’s (MSc) degree.
Starting with the PGCert, you will complete 60 CAT points. Upon successful completion, you will have the opportunity to progress to the Postgraduate Diploma (PGDip), and finally, complete the MSc by undertaking the remaining project module and dissertation.
This flexible structure allows you to balance your studies with your professional and personal commitments while still working toward a full MSc qualification.
ABOUT YOU –
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.
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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.
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.
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.
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.
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.
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 projects 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
The Course is composed of three distinct modules, each intended to progressively enhance your knowledge, comprehension, and proficiencies in AI. Each module begins with a week-long pre-module onboarding reading activity, followed by an intensive block mode consisting of onsite delivery of lectures and lab activities, spanning 2-3 days each week for three weeks. Project-based assessment is conducted following the taught period. To supplement the onsite sessions, there are substantial digital resources, such as pre-lecture videos and video-ed labs, with online support sessions available beyond the onsite teaching periods.
Transferrable Skills
The objective of this course is to produce highly trained and desirable graduates who are well-prepared for the rapidly-evolving field of AI technology. Upon successfully finishing the PG Cert program, students will be qualified to pursue a full MSc in AI.
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.
Assessment
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.
The information below is intended as an example only, featuring module details for the current year of study (2024/25). 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.
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.
AICC/NI Cyber 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. Applicants are advised to apply as early as possible in order to be considered for a funded place. You will be notified as soon as possible 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.
In the event that any programme receives a high number of applications, the University reserves the right to close the application portal prior to the deadline stated on course finder. Notifications to this effect will appear on the application portal against the programme application page.
Please note: a deposit will be required to secure a place.
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 6.5, 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.
If you need to improve your English language skills before you enter this degree programme, Queen's University Belfast International Study Centre 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.
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.
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.
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 (excluding Initial Teacher Education/PGCE, where undergraduate student finance is available). Criteria, eligibility, repayment and application information are available on the UK government website.
Traditional Computer Vision
(a selection of the following)
Introduction
Image acquisition; Image representations; Image resolution, sampling and quantisation; Colour models
Representation for Matching and Recognition
Histograms, thresholding, enhancement; Convolution and filtering
Scale Invariant Feature Transform (SIFT)
Hough transforms
Geometric hashing
Image representation and filtering in the frequency domain; JPEG and MPEG compression
Neural networks
Loss functions and stochastic gradient descent;
Backpropagation; Architecture of Neural Network and different activation functions;
Issues with training Neural Networks
Autograd; Hyperparameter optimisation
Deep Learning for Computer Vision
(a selection of the following)
Convolutional Neural Networks: image classification
Generative adversarial networks: image generation
Residual Networks (ResNet)
YOLO: object detection
Vision Transformer
Case studies
Reading List:
Szeliski, R. Computer Vision: Algorithms and Applications. 2011, Springer.
Ian Goodfellow, Yoshua Bengio and Aaron Courville. Deep Learning, 2017, MIT Press.
Learning Outcomes
Successful students will be able to:
1. Utilise principles and theories of Computer Vision in selected scenarios,
2. Design and implement Computer Vision techniques to solve practical problems using software tools and libraries.
3. Evaluate potential solutions to problems using image and video data.
Skills
Ability to utilise deep learning algorithms and techniques to solve real-world computer vision challenges. Creativity in obtaining image/video data from recognised repositories. Ability to utilise existing libraries and packages for implementing appropriate machine learning models for a given computer vision task. Recognising patterns in image/video data in a convincing way.
Coursework
100%
Examination
0%
Practical
0%
Credits
20
Module Code
ECS8053
Teaching Period
Spring
Duration
4 weeks
Pre-requisite
No
Core/Optional
Core
Machine Learning
Overview
Core concepts in machine learning via linear regression
• The machine learning workflow; design and analysis of machine learning experiments
• Linear regression: least-squares and maximum likelihood
• Generalisation: overfitting, regularisation and the bias-variance trade-off
Classification
• Classification algorithms: k-NN, logistic regression, decision trees, support vector machine,
ensemble learning
• Evaluation metrics for classification models
• Explainable AI (XAI): feature attribution methods for black-box algorithms
Bayesian machine learning and probabilistic programming
• Bayesian approach to machine learning; Bayesian linear regression
• Bayesian non-parametric models: Gaussian Process regression
• Probabilistic programming; Markov Chain Monte Carlo methods and diagnostics
Unsupervised learning
• Clustering algorithms: k-means, hierarchical clustering, density-based clustering
• Evaluation metrics for clustering algorithms
• Dimensionality reduction: PCA and PLS
Case studies
Reading List:
Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach, 2020, Pearson
Tom M. Mitchell. Machine Learning, McGraw-Hill
Learning Outcomes
Successful students will be able to:
1. Demonstrate critical understanding of the theory underpinning core concepts and algorithms in
machine learning
2. Evaluate and compare supervised and unsupervised learning algorithms on problems involving
real datasets
3. Diagnose and rectify common problems that affect the performance of machine learning
algorithms
4. Design machine learning experiments and justify the procedures employed
Skills
Ability to identify problems that can be solved using machine learning methods. Ability to apply suitable classical machine learning algorithms and software packages to solve real-world problems. Ability to evaluate and compare the performance of machine learning methods for a given problem.
Present and discuss the results of machine learning methods and propose appropriate improvements to methods.
Coursework
100%
Examination
0%
Practical
0%
Credits
20
Module Code
ECS8051
Teaching Period
Autumn
Duration
4 weeks
Pre-requisite
No
Core/Optional
Core
Foundations of AI
Overview
Constraint satisfaction
Markov decision processes
Probability and Statistics
Random variables
Conditional and joint distributions
Variance and expectation
Bayes Theorem and its applications
Law of large numbers and the Multivariate Gaussian distribution
Calculus:
Differential and integral calculus, partial derivatives, vector-values functions, directional gradient
Optimisation
Convexity
1-D minimisation
Gradient methods in higher dimensions
Linear Algebra
Using matrices to find solutions of linear equations
Properties of matrices and vector spaces
Eigenvalues, eigenvectors and singular value decomposition
Learning Outcomes
Successful students will be able to:
1. Demonstrate knowledge and critical understanding of topics from linear algebra, calculus, probability, statistics and optimisation that are required to apply in AI
2. Relate the mathematics topics to
Skills
Ability to utilise fundamental mathematics underlying AI and identify the most suitable modelling, optimisation, factorisation, and transformation approach for a given problem.
