MSc Advanced Computer Science (Artificial Intelligence)

Programme overview

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.
  

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.

Year 1

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

Compulsory Modules

Candidates will be required to study the following compulsory modules:

Code	Title	Credits	Semester	Pass for Progression
COMP5200M	MSc Project	60	1 Jan to 30 Sep	PFP
COMP5450M	Knowledge Representation and Reasoning	15	Semester 1 (Sep to Jan)
COMP5611M	Machine Learning	15	Semester 2 (Jan to Jun)
COMP5625M	Deep Learning	15	Semester 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:

Code	Title	Credits	Semester	Pass for Progression
COMP5122M	Data Science	15	Semester 1 (Sep to Jan)
COMP5123M	Cloud Computing Systems	15	Semester 2 (Jan to Jun)
COMP5125M	Blockchain Technologies	15	Semester 2 (Jan to Jun)
COMP5400M	Bio-Inspired Computing	15	Semester 2 (Jan to Jun)
COMP5710M	Algorithms	15	Semester 1 (Sep to Jan)
COMP5712M	Programming for Data Science	15	Semester 1 (Sep to Jan)
COMP5840M	Data Mining and Text Analytics	15	Semester 2 (Jan to Jun)
COMP5911M	Advanced Software Engineering	15	Semester 1 (Sep to Jan)
COMP5930M	Scientific Computation	15	Semester 1 (Sep to Jan)
COMP5940M	Graph Theory: Structure and Algorithms	15	Semester 2 (Jan to Jun)

Last updated: 19/09/2024 16:36:14

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