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.

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.

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 .
  
  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.

Modules

• Year 1

  Core Modules

  Computer Vision (20 credits)

  ×

  Computer Vision

  Overview

  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

  Natural Language Processing (20 credits)

  ×

  Natural Language Processing

  Overview

  Basics of Natural Language Processing
  Lexical, syntactic, semantic and discourse representations
  Language modelling
  Grammar
  
  Distributed Representations
  Distributional semantics
  Word representations based on vector space models such as word2vec and GloVe.
  
  Deep Learning Architectures for NLP
  Convolutional Neural Network
  Recurrent Neural Networks
  Transformers and self-attention
  
  Applications and current topics (to be selected from the following):
  Text mining, text classification/clustering
  Named entity recognition
  Machine translation
  Question answering
  Automatic summarisation
  Topic modelling
  Explainability
  
  Case studies
  
  Reading List:
  Yoav Goldberg. Neural Network Methods in Natural Language Processing, 2017
  Lewis Tunstall, Leandro von Werra, Thomas Wolf (2022). Natural Language Processing with
  Transformers. O'Reilly Media, Inc. ISBN: 9781098103248
  Uday Kamath, John Liu, James Whitaker. Deep Learning for NLP and Speech Recognition 2019 Edition, Springer.

  Learning Outcomes

  Successful students will be able to:
  1. Demonstrate critical understanding of the fundamental principles, algorithms and tools for natural language processing.
  2. Design and implement natural language processing techniques to solve novel and practical problems and evaluate the usability of the artefacts.
  3. Evaluate natural language processing systems.

  Skills

  Ability to utilise deep learning algorithms and techniques to solve real-world NLP challenges. Creativity in obtaining text/speech data from recognised repositories. Ability to utilise existing libraries and packages for developing NLP models. Understanding of current developments, methods and applications of NLP.

  Coursework

  100%

  Examination

  0%

  Practical

  0%

  Credits

  20

  Module Code

  ECS8054

  Teaching Period

  Spring

  Duration

  4 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Themed Research Project (60 credits)

  ×

  Themed Research Project

  Overview

  The topic of each Project should be drawn from the following thematic areas of artificial intelligence (AI) covered by the Programme: machine 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, biomedical informatics). 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.
  The project should consolidate and build on earlier work in the programme, and may involve collaborations with other disciplines, such as health, biomedical sciences, environmental or social sciences.

  Learning Outcomes

  Successful students will be able to:
  1. Employ a range of communication skills, including the effective use of technology, to manage and disseminate knowledge to appropriate audiences.
  2. Plan and implement change through research and project management, with adherence to ethical guidelines.
  3. Design, implement and test a chosen solution according to professional best practice.
  4. Analyse and critically evaluate concepts, arguments and evidence in the literature.
  5. Beware the current AI issues (e.g., trust, ethics, interpretability, human centred AI, and AI for good).

  Skills

  Entrepreneurial, interpersonal and communication skills;
  
  Critical thinking and team working skills;
  
  Research skills (including the ability to search for, locate, extract, organise, evaluate and use or present information that is relevant to a particular topic);
  
  Ability to work independently.

  Coursework

  100%

  Examination

  0%

  Practical

  0%

  Credits

  60

  Module Code

  ECS8056

  Teaching Period

  Summer

  Duration

  12 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Machine Learning (20 credits)

  ×

  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

  Knowledge Engineering (20 credits)

  ×

  Knowledge Engineering

  Overview

  Logic
  Propositional logic; First order logic
  
  Knowledge and knowledge representation
  Formal concept analysis; Description logics and ontologies; OWL; Knowledge graph
  
  Reasoning under Uncertainty
  Probabilities, conditional independence; Causality; Evidential theory; Bayesian networks
  
  Decision theory
  Case study -- Clinical decision support
  
  
  Reading List:
  Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach, 2020, Pearson
  Martin Peterson. An Introduction to Decision Theory, 2017, Cambridge.

  Learning Outcomes

  Successful students will be able to:
  1. Gain a systematic understanding of knowledge, principles and procedures of knowledge engineering
  2. Demonstrate a critical awareness and evaluation of current and complex issues and developments in knowledge engineering
  3. Demonstrate critical understanding of research techniques in knowledge engineering and ability to critically evaluate them
  4. Demonstrate innovative application of the knowledge, principles and procedures of knowledge engineering in making sound judgements and proposing new hypotheses

  Skills

  Ability to utilise suitable knowledge-based methods to solve real-world problems. Ability to evaluate and compare the performance of KB solutions for a given problem.

  Coursework

  100%

  Examination

  0%

  Practical

  0%

  Credits

  20

  Module Code

  ECS8052

  Teaching Period

  Autumn

  Duration

  4 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Foundations of AI (20 credits)

  ×

  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.

  Coursework

  100%

  Examination

  0%

  Practical

  0%

  Credits

  20

  Module Code

  ECS8050

  Teaching Period

  Autumn

  Duration

  4 weeks

  Pre-requisite

  No

  Core/Optional

  Core

  Artificial Intelligence for Health (20 credits)

  ×

  Artificial Intelligence for Health

  Overview

  - Data in healthcare services and treatments
  - AI in precision medicine
  - AI for medical diagnostic
  - AI in medical imaging
  - Application of AI in drug discovery and development
  - Technical, legal and ethical challenges of using AI in healthcare

  Learning Outcomes

  Successful students will be able to:
  1. Demonstrate knowledge, critical understanding, and application of AI for health
  2. Explain how AI is used to address healthcare problems; Relate healthcare problems and data to appropriate AI algorithms
  3. Apply AI principles and methods to address some of the healthcare problems in different contexts
  4. Discuss and evaluate critically current challenges with AI implementations in healthcare including ethics

  Skills

  Ability to utilise AI principles and techniques to solve some health challenges. Creativity in obtaining relevant data from recognised repositories. Ability to utilise existing libraries and packages for analysing and visualising health data. Recognising patterns in data in a convincing way.

  Coursework

  100%

  Examination

  0%

  Practical

  0%

  Credits

  20

  Module Code

  ECS8055

  Teaching Period

  Spring

  Duration

  4 weeks

  Pre-requisite

  No

  Core/Optional

  Core

Entrance requirements

Graduate

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 http://go.qub.ac.uk/RPLpolicy 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.
https://www.qub.ac.uk/about/Leadership-and-structure/Faculties-and-Schools/Engineering-and-Physical-Sciences/AICC/

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.

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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: www.qub.ac.uk/EnglishLanguageReqs.

Career Prospects

Introduction

-

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 to Apply

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

Apply now

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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