Course Structure

Taught modules will be running in block mode.

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.

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 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 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.

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

  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

  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

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.

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 6.5, 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.

For more information on English Language requirements for EEA and non-EEA nationals see: www.qub.ac.uk/EnglishLanguageReqs.

Career Prospects

Introduction

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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	Free for DfE Funded students (see below)
Republic of Ireland (ROI) 2	N/A
England, Scotland or Wales (GB) 1	N/A
EU Other 3	N/A
International	N/A

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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