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2023/24 Taught Postgraduate Programme Catalogue

MSc Advanced Computer Science (Artificial Intelligence)

Programme code:MSC-ACS/AI-FUCAS code:
Duration:12 Months Method of Attendance: Full Time
Programme manager:Dr Brandon Bennett Contact

Total credits: 180

Entry requirements:

A minimum UK Upper Second Class Honours (2.1) degree or equivalent in computing or a scientific subject with significant computing component;
- A pass at GCSE level English Language (grade C or above);
- International students must have an English language qualification at a suitable level: IELTS 6.5 or equivalent.

School/Unit responsible for the parenting of students and programme:

School of Computing

Examination board through which the programme will be considered:

School of Computing

Relevant QAA Subject Benchmark Groups:


Programme specification:

On completion of the programme students should be a able to demonstrate:

- a systematic understanding of the theory and practice of designing and implementing computer systems
- proficiency in the technical and programming skills required to design and implement computer systems;
- a thorough knowledge and skills base in a number of advanced topics within the domain of computer science;
- an in-depth knowledge of the essential principles and practices used in the effective design, implementation and usability of Intelligent Systems;
- the ability to apply these principles and practices to tackle a significant problem within the main project;
- an in-depth understanding of an area of specialisation, gained during the main project;
- be confident in applying the research methodology adopted for the main project on new problems;
- be prepared for further study either in the context of professional development or through further engagement in higher education.

The programme will:

- situate the study of Intelligent Systems within the general context of computational modelling and complex systems.
- give a broad perspective on Intelligent systems, covering evolutionary models, statistical and symbolic machine learning algorithms, qualitative reasoning, image processing, language understanding, and bio-computation.
-be rooted in established research strengths of the School and will offer the opportunity for students to work as integral members of our research groups during their main project.
- prepare graduates for graduate careers in the IT industry and other contexts or for further study either in the context of continuing professional development or through further engagement in higher education.

Year1 - View timetable

[Learning Outcomes, Transferable (Key) Skills, Assessment]

Compulsory modules:

Candidates will be required to study the following compulsory modules:

COMP5200MMSc Project60 credits1 Jan to 30 Sep
COMP5450MKnowledge Representation and Reasoning15 creditsSemester 1 (Sep to Jan)
COMP5611MMachine Learning15 creditsSemester 2 (Jan to Jun)
COMP5625MDeep Learning15 creditsSemester 2 (Jan to Jun)

Optional modules:

Candidates will be required to study 75 credits from the following lists of optional modules:

Candidates will be required to study 75 credits from the following list of optional modules:

COMP5122MData Science15 creditsSemester 1 (Sep to Jan)
COMP5123MCloud Computing Systems15 creditsSemester 2 (Jan to Jun)
COMP5125MBlockchain Technologies15 creditsSemester 2 (Jan to Jun)
COMP5400MBio-Inspired Computing15 creditsSemester 2 (Jan to Jun)
COMP5710MAlgorithms15 creditsSemester 1 (Sep to Jan)
COMP5712MProgramming for Data Science15 creditsSemester 1 (Sep to Jan)
COMP5840MData Mining and Text Analytics15 creditsSemester 2 (Jan to Jun)
COMP5911MAdvanced Software Engineering15 creditsSemester 1 (Sep to Jan)
COMP5930MScientific Computation15 creditsSemester 1 (Sep to Jan)
COMP5940MGraph Theory: Structure and Algorithms15 creditsSemester 2 (Jan to Jun)

Last updated: 28/04/2023 14:30:42


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