Courses

Programme Specification

MSc Artificial Intelligence

Academic Year 2023/24

A programme specification is required for any programme on which a student may be registered. All programmes of the University are subject to the University's Quality Assurance processes. All degrees are awarded by Queen's University Belfast.

Programme Title

MSc Artificial Intelligence

Final Award
(exit route if applicable for Postgraduate Taught Programmes)

Master of Science

Programme Code

CSC-MSC-AI

UCAS Code

HECoS Code

100359 - Artificial intelligence - 100

ATAS Clearance Required

Mode of Study

Full Time or Part Time

Type of Programme

Postgraduate

Length of Programme

Full Time - 1 Academic Year
Part Time - 3 Academic Years

Total Credits for Programme

180

Exit Awards available

Institute Information

Teaching Institution	Queen's University Belfast
School/Department	Electronics, Electrical Engineering & Computer Science
Quality Code https://www.qaa.ac.uk/quality-code Higher Education Credit Framework for England https://www.qaa.ac.uk/quality-code/higher-education-credit-framework-for-england	Level 7
Subject Benchmark Statements https://www.qaa.ac.uk/quality-code/subject-benchmark-statements The Frameworks for Higher Education Qualifications of UK Degree-Awarding Bodies https://www.qaa.ac.uk/docs/qaa/quality-code/qualifications-frameworks.pdf	Computing (2007)
Accreditations (PSRB)
No accreditations (PSRB) found.

Regulation Information

Does the Programme have any approved exemptions from the University General Regulations
(Please see General Regulations)

Programme Specific Regulations

Modules will be taught in block mode.

The pass mark for taught modules is 50%.

Students will only have the opportunity to resit failed modules once at the next available opportunity, with the mark used in calculating the final award capped at 50%.

Students who, at the first attempt, have not achieved satisfactory performance in all specified compulsory elements in modules with a combined value greater than 40 CATS points will normally be required to transfer to the Postgraduate Certificate.

Students who fail the same module twice will normally be required to transfer to the Postgraduate Certificate, and may not progress to complete the Individual Research Project.

Normally, students must have satisfactorily completed all required taught modules (120 CATS points) to be permitted to complete the Individual Research Project.

The pass mark for the research project module is 50%. The programme will follow the school’s standard assessment moderation processes. Specifically, all assessments will be internally moderated and then moderated by the external examiner. The exam scripts / projects etc will then marked, internally moderated and made available for the external examiner to moderate. Projects are marked separately by the project supervisor and a second marker. The project mark is agreed upon at a project marking meeting, if unable to agree on a mark a third marker will be called upon. All marks will be presented for further scrutiny to the Board of Examiners.

Students with protected characteristics

Support For Students and Their Learning Systems Designed to Provide General Pastoral and Academic Guidance:
1. All students are allocated a Project Supervisor who provides general academic and personal support and encouragement, and advice on pastoral issues.
2. The Course Co-ordinator provides advice and support for 'students at risk' (i.e., those considered to be at risk of failing examinations or who have serious personal, academic or health problems). The Course Co-ordinator will also deal with cases referred by Project Supervisors.
3. A female member of staff is available for consultation by female students.
4. All students have access to the University Health and Counselling Services, Students' Union, University Careers Service and Student Support Services.
5. All students have access to the University Harassment Advisers.
6. Under University Regulations designated procedures are in place to process complaints made by students.

Systems Designed to Support Students' Experience of the Learning and Teaching Process
1. An induction programme for new students is held during the first week of Semester 1.
2. All students receive a copy of the handbook for the course.
3. All students have access to the Queen's intranet services which offers:
(i) E-mail communication with staff.
(ii) Access to learning and teaching materials (i.e., syllabi, lecture and tutorial outlines and other course materials).
(iii) Opportunities to participate in discussion with teachers and other students.
4. Students have access to University libraries and Student Computer Centres.
5. Staff/Student Consultative Committee provides a forum for consultation and discussion between staff and students. SCC is convened at least once each semester.
6. Staff will communicate regularly with students during any period of summer internship to provide support and guidance.
7. Facilities are available within the School and adjacent teaching facilities to aid students with physical disabilities; the School also adheres to University policy concerning the support of students with sensory, learning and physical disabilities

Are students subject to Fitness to Practise Regulations

(Please see General Regulations)

Educational Aims Of Programme

MSc Artificial Intelligence (AI) is aimed 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 the AI knowledge and skills to develop AI systems and applications.
Students who exit the programme with a Postgraduate Diploma (Exit Award) will have been introduced to all the core taught material, achieved a good understanding of the range of topics, and acquired skills associated with the creation, evaluation and deployment of AI systems and applications.

Learning Outcomes

Learning Outcomes: Cognitive Skills

On the completion of this course successful students will be able to:

CS1. Synthesise and critically review knowledge and data from a range of sources to enable the specification, design and use of statistical, machine learning, computer vision, natural language processing, knowledge engineering models for AI solutions.

Teaching/Learning Methods and Strategies

Develop primarily in modules where knowledge and data are needed, as well as the project.

Methods of Assessment

Combination of written examinations (CS1), practical work (CS1), coursework (CS1, CS2, CS3), presentations (CS2, CS3) and dissertation (CS1, CS2, CS3).

CS2. Assess the implications and risks of applying AI solutions to specific application domains.

Teaching/Learning Methods and Strategies

Develop primarily in modules with practical components, as well as the project.

Methods of Assessment

Combination of written examinations (CS1), practical work (CS1), coursework (CS1, CS2, CS3), presentations (CS2, CS3) and dissertation (CS1, CS2, CS3).

CS3. Recognise and be able to respond to opportunities for innovation in an appropriate way.

Teaching/Learning Methods and Strategies

Develop primarily in modules with practical components, as well as the project.

Methods of Assessment

Combination of written examinations (CS1), practical work (CS1), coursework (CS1, CS2, CS3), presentations (CS2, CS3) and dissertation (CS1, CS2, CS3).

Learning Outcomes: Knowledge & Understanding

On the completion of this course successful students will be able to:

KU1. Demonstrate systematic and critical understanding of the advanced theories, concepts, paradigms, and algorithms underpinning the AI domain

Teaching/Learning Methods and Strategies

Being a core part of the whole programme, this is strongly developed across all modules.

Methods of Assessment

Combination of written examinations (KU1), practical work (KU2), coursework (KU1, KU2, KU3), presentations (KU1, KU3) and dissertation (KU1, KU2, KU3, KU4).

KU2. Effectively use practices and tools for the specification, design, implementation, and critical evaluation of AI models

Teaching/Learning Methods and Strategies

Being a core part of the whole programme, this is strongly developed across all modules, especially those with a practical component, as well as the project.

Methods of Assessment

Combination of written examinations (KU1), practical work (KU2), coursework (KU1, KU2, KU3), presentations (KU1, KU3) and dissertation (KU1, KU2, KU3, KU4).

KU3 Demonstrate a critical awareness of the professional, legal and ethical issues associated with the deployment of AI systems and applications

Teaching/Learning Methods and Strategies

Primarily developed in those with a practical component, in particular the project.

Methods of Assessment

Combination of written examinations (KU1), practical work (KU2), coursework (KU1, KU2, KU3), presentations (KU1, KU3) and dissertation (KU1, KU2, KU3, KU4).

KU4. Synthesise knowledge, principles and practices in AI and apply these in a real-world research problem

Teaching/Learning Methods and Strategies

Primarily developed in those with a practical component, in particular the project.

Methods of Assessment

Combination of written examinations (KU1), practical work (KU2), coursework (KU1, KU2, KU3), presentations (KU1, KU3) and dissertation (KU1, KU2, KU3, KU4).

Learning Outcomes: Subject Specific

On the completion of this course successful students will be able to:

SS1. Apply a range of AI theories and concepts to understand and analyse complex AI systems.

Teaching/Learning Methods and Strategies

Strongly developed in all other modules and reinforce through research project

Methods of Assessment

Combination of unseen written examinations (SS1), practical work (SS1, SS2, SS3, SS4, SS5), coursework (SS3, SS4, SS5), presentations (SS5) and dissertation (SS1, SS2, SS3, SS4, SS5).

SS2. Effectively use appropriate AI tools for the development and testing of AI systems.

Teaching/Learning Methods and Strategies

Strongly developed in modules that have an emphasis on laboratory work and also strongly developed in the research project

Methods of Assessment

Combination of unseen written examinations (SS1), practical work (SS1, SS2, SS3, SS4, SS5), coursework (SS3, SS4, SS5), presentations (SS5) and dissertation (SS1, SS2, SS3, SS4, SS5).

SS3. Use appropriate AI theory and practices for the specification, design and evaluation of an AI system.

Teaching/Learning Methods and Strategies

Very strongly addressed across the whole programme.

Methods of Assessment

Combination of unseen written examinations (SS1), practical work (SS1, SS2, SS3, SS4, SS5), coursework (SS3, SS4, SS5), presentations (SS5) and dissertation (SS1, SS2, SS3, SS4, SS5).

SS4. Implement algorithms using programming languages to solve complex AI problems.

Teaching/Learning Methods and Strategies

Strongly developed across the whole programme, particularly those with components underlying the focused AI applications.

Methods of Assessment

Combination of unseen written examinations (SS1), practical work (SS1, SS2, SS3, SS4, SS5), coursework (SS3, SS4, SS5), presentations (SS5) and dissertation (SS1, SS2, SS3, SS4, SS5).

SS5. Articulate and effectively communicate the design and technological rationale for a given AI component through appropriate technical reports and presentations.

Teaching/Learning Methods and Strategies

Strongly developed across the whole programme, particularly those components underlying the focused applications.

Methods of Assessment

Combination of unseen written examinations (SS1), practical work (SS1, SS2, SS3, SS4, SS5), coursework (SS3, SS4, SS5), presentations (SS5) and dissertation (SS1, SS2, SS3, SS4, SS5).

Learning Outcomes: Transferable Skills

On the completion of this course successful students will be able to:

TS1. Utilise digital and other learning resources and information retrieval skills to acquire, summarise and critically appraise information relevant to the AI domain

Teaching/Learning Methods and Strategies

Strongly developed across the whole programme, particularly those including practical components and the project.

Methods of Assessment

Combination of written examinations (TS2), practical work (TS1, TS2, TS3, TS4, TS5), coursework (TS1, TS2, TS3, TS4, TS5), presentations (TS2, TS3, TS5) and dissertation (TS1, TS2, TS3, TS4, TS5).

TS2. Demonstrate mastery of the translational skills necessary to communicate using rational and complex arguments, using a variety of media to technical and non-technical audiences

Teaching/Learning Methods and Strategies

Strongly developed across the whole programme, particularly those including practical components and the project.

Methods of Assessment

Combination of written examinations (TS2), practical work (TS1, TS2, TS3, TS4, TS5), coursework (TS1, TS2, TS3, TS4, TS5), presentations (TS2, TS3, TS5) and dissertation (TS1, TS2, TS3, TS4, TS5).

TS3. Demonstrate self-direction and originality in tackling and solving problems, and act autonomously in planning and implementing tasks to a professional standard

Teaching/Learning Methods and Strategies

Strongly developed across the whole programme, particularly the project.

Methods of Assessment

Combination of written examinations (TS2), practical work (TS1, TS2, TS3, TS4, TS5), coursework (TS1, TS2, TS3, TS4, TS5), presentations (TS2, TS3, TS5) and dissertation (TS1, TS2, TS3, TS4, TS5).

TS4. Show originality and innovation and recognise the need for continuing professional development in the application of knowledge and techniques for the development of AI systems

Teaching/Learning Methods and Strategies

Strongly developed across the whole programme, particularly the project.

Methods of Assessment

Combination of written examinations (TS2), practical work (TS1, TS2, TS3, TS4, TS5), coursework (TS1, TS2, TS3, TS4, TS5), presentations (TS2, TS3, TS5) and dissertation (TS1, TS2, TS3, TS4, TS5).

TS5. Independently produce data and associated research outputs which effectively inform and engage audiences

Teaching/Learning Methods and Strategies

Primarily developed through the project.

Methods of Assessment

Combination of written examinations (TS2), practical work (TS1, TS2, TS3, TS4, TS5), coursework (TS1, TS2, TS3, TS4, TS5), presentations (TS2, TS3, TS5) and dissertation (TS1, TS2, TS3, TS4, TS5).

Module Information

Stages and Modules

Module Title	Module Code	Level/ stage	Credits	Availability		Duration	Pre-requisite			Assessment
				S1	S2			Core	Option	Coursework %	Practical %	Examination %
Knowledge Engineering	ECS8052	1	20	YES	--	4 weeks	N	YES	--	100%	0%	0%
Natural Language Processing	ECS8054	1	20	--	YES	4 weeks	N	YES	--	100%	0%	0%
Machine Learning	ECS8051	1	20	YES	--	4 weeks	N	YES	--	100%	0%	0%
Computer Vision	ECS8053	1	20	--	YES	4 weeks	N	YES	--	100%	0%	0%
Themed Research Project	ECS8056	1	60	--	YES	12 weeks	N	YES	--	100%	0%	0%
Foundations of AI	ECS8050	1	20	YES	--	4 weeks	N	YES	--	100%	0%	0%
Artificial Intelligence for Health	ECS8055	1	20	--	YES	4 weeks	N	YES	--	100%	0%	0%

Notes

No notes found.