Introduction
The MEng Computing Science (Artificial Intelligence) is a five-year integrated Master's programme that combines our four-year BSc Computing Science with an additional year of postgraduate-level study. This extra year enhances your ability to solve real-world problems while deepening your expertise in the growing field of AI.
Study Information
At a Glance
- Learning Mode
- On Campus Learning
- Degree Qualification
- MEng
- Duration
- 60 months
- Study Mode
- Full Time
- Start Month
- September
- Location of Study
- Aberdeen
- UCAS Code
- I105
This programme is structured to provide a seamless transition from undergraduate to postgraduate study within a single degree. The additional year of study helps you gain a competitive edge in your career.
At the University of Aberdeen, Computing Science encompasses both the theoretical and practical aspects of the field, with a strong emphasis on developing technical analysis, design, and programming skills. You will explore subjects including software programming, databases and data management, computer systems, AI, and cybersecurity. These skills are applied to a variety of commercial, scientific, and socio-economic contexts.
Our teaching methodology reflects the ongoing advancements in computing, which are continually reshaping how we live, learn, work, and socialise. From detecting and treating diseases to analysing business, scientific, or social data, and making online shopping more secure, our curriculum is designed to prepare you for the future.
You will be taught by leading researchers whose work in multi-agent systems, natural language generation, machine learning, and blockchain technology informs the content of your lectures.
Our strong industry connections further enhance your learning experience through guest lectures, seminars, and industry-sponsored prizes from notable organisations like Amazon, CGI, and ScotlandIS. Additionally, you can undertake a placement during your studies, providing an excellent opportunity to gain practical professional experience.
The fifth year of this programme covers all aspects of AI, from fundamental theories to cutting-edge techniques, offering career opportunities in this rapidly evolving field including data and text mining, machine learning, reasoning, natural language generation, knowledge representation, and distributed AI systems. You will learn to engineer and evaluate AI systems and engage in discussions about the legal and ethical considerations of AI technology.
What You'll Study
- Year 1
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Compulsory Courses
- Getting Started at the University of Aberdeen (PD1002)
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This course, which is prescribed for level 1 undergraduate students (and articulating students who are in their first year at the University), is studied entirely online, takes approximately 5-6 hours to complete and can be taken in one sitting, or spread across a number of weeks.
Topics include orientation overview, equality and diversity, health, safety and cyber security and how to make the most of your time at university in relation to careers and employability.
Successful completion of this course will be recorded on your Enhanced Transcript as ‘Achieved’.
- Programming 1 (CS1032)
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15 Credit Points
This course will be delivered in two halves. The first half will provide a self-contained introduction to computer programming. It will be accessible to all undergraduates. Students will be exposed to the basic principles of computer programming, e.g. fundamental programming techniques, concepts, algorithms and data structures. The course contains lectures where the principles are systematically developed. As the course does not presuppose knowledge of these principles, we start from basic intuitions. The second half will be particularly of use to those studying Science and Engineering subjects, broadly interpreted, as well as Computing and IT specialists. It will include a gentle introduction to professional issues and security concepts.
- Modelling and Problem Solving for Computing (CS1029)
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15 Credit Points
This course will introduce students to techniques that support problem solving and modelling with computers, and concepts and methods that are fundamental to computing science. The techniques and concepts will be illustrated with numerous computing examples.
- Web Development (CS1534)
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15 Credit Points
Students will learn to develop modern web applications using a variety of languages and frameworks as part of their degree, and prepare them for whatever they do after graduation. A key focus will be on the integration of HTML with CSS and Javascript with other backing frameworks to develop dynamic applications. The course is open to all undergraduates, and is accessible to those with no previous experience.
- Object - Oriented Programming (CS1527)
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15 Credit Points
This course will build on the basic programming skills acquired in the first half-session and equip the students with advanced object oriented programming knowledge, implementation of data structure and algorithms, and basic software engineering techniques. The students will be challenged with more complicated programming problems through a series of continuous assessments.
Optional Courses
Plus, select a further 60 credit points from courses of choice.
- Year 2
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Compulsory Courses
- Software Programming (CS2020)
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15 Credit Points
This course is concerned with tools and techniques for scalable and dependable software programming. It focusses primarily on the Java programming language and related technologies. The course gives extensive programming practice in Java. It covers in depth features of the language and how best to use them, the execution model of the language, memory management, design principles underpinning the language, and comparisons with other languages. Tools for collaboration, productivity, and versioning will also be discussed.
- Databases and Data Management (CS2019)
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15 Credit Points
Databases are an important part of traditional information systems (offline /online) as well as modern data science pipelines. This course will be of interest to anyone who wishes to learn to design and query databases using major database technologies. The course aims to teach the material using case studies from real-world applications, both in lectures and lab classes.
In addition, the course covers topics including management of different kinds of data such as spatial data and data warehousing. The course provides more hands-on training that develops skills useful in practice.
- Human - Computer Interaction (CS2506)
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15 Credit Points
This course looks at why a computer system that interacts with human beings needs to be usable. It covers a set of techniques that allow usability to be taken into account when a system is designed and implemented, and also a set of techniques to assess whether usability has been achieved. Weekly practical sessions allow students to practice these techniques. The assessed coursework (which is normally carried out by groups of students) gives an opportunity to go through the design process for a concrete computer system, with a particular focus on ensuring usability.
- Algorithms and Data Structures (CS2522)
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15 Credit Points
This course provides the knowledge needed to understand, design and compare algorithms. By the end of the course, a student should be able to create or adapt algorithms to solve problems, determine an algorithm's efficiency, and be able to implement it. The course also introduces the student to a variety of widely used algorithms and algorithm creation techniques, applicable to a range of domains. The course will introduce students to concepts such as pseudo-code and computational complexity, and make use of proof techniques. The practical component of the course will build on and enhance students' programming skills.
Optional Courses
Plus, select a further 60 credit points from courses of choice.
- Year 3
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Compulsory Courses
- Artificial Intelligence (CS3033)
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15 Credit Points
The course provides an introduction to Artificial Intelligence (AI). It discusses fundamental problems of AI and their computational solution via key concepts.
- Operating Systems (CS3026)
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15 Credit Points
This course discusses core concepts and architectures of operating systems, in particular the management of processes, memory and storage structures. Students will learn about the scheduling and operation of processes and threads, problems of concurrency and means to avoid race conditions and deadlock situations. The course will discuss virtual memory management, file systems and issues of security and recovery. In weekly practical session, students will gain a deeper understanding of operating system concepts with various programming exercises.
- Principles of Software Engineering (CS3028)
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15 Credit Points
Students will develop large commercial and industrial software systems as a team-based effort that puts technical quality at centre stage. The module will focus on the early stage of software development, encompassing team building, requirements specification, architectural and detailed design, and software construction. Group work (where each team of students will develop a system selected using a business planning exercise) will guide the software engineering learning process. Teams will be encouraged to have an active, agile approach to problem solving through the guided study, evaluation and integration of practically relevant software engineering concepts, methods, and tools.
- Enterprise Computing and Business (CS3525)
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15 Credit Points
This course provides insight into the business reasons for large software systems such as loyalty card systems, backend systems integrating firms and their suppliers and larges systems that integrate payroll, finance and operational parts of a business. You also learn the entrepreneurial aspects of business during the practical sessions where you explore and develop your own business application idea using service design and lean startup approaches centred around customer development, which you will find useful in any future work. This course is open to anyone across the university and requires no programming experience.
- Software Engineering and Professional Practice (CS3528)
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15 Credit Points
In this module, which is the follow-up of CS3028, students will focus on the team-based development of a previously specified, designed, and concept-proofed software system. Each team will build their product to industrial-strength quality standards following an agile process and applying the software engineering concepts, methods, and tools introduced in CS3028. The course includes a series of mandatory participatory seminars on professional and management issues in IT and IT projects. Students will be expected to relate their engineering work to these issues.
Optional Courses
Plus 30 credit points from courses of choice.
- Year 4
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Compulsory Courses
- Research Methods (CS4040)
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15 Credit Points
In this course, you will conduct an individual research project into the behaviour of a computing system. You will develop knowledge and understanding of rigorous methods to: explore computing system behaviour; identify questions about behaviour; design experiments to answer those questions; analyse experimental results; and report on the outcomes of your research. You will develop your understanding of research ethics and how this relates to professional behaviour.
- Security (CS4028)
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15 Credit Points
The course provides a solid foundation in computer and information security. It will cover topics of Information and Risk, Threats and Attacks, Cybersecurity Architecture and Operations, Secure Systems and Products, Cybersecurity Management and Trustworthy Software.
- Introduction to Machine Learning and Data Mining (CS4049)
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15 Credit Points
This course provides an introduction to machine learning and data mining. Students will learn how to analyse complex datasets by applying data pre-processing, exploration, clustering and classification, time-series analysis, neural networks, and many other techniques. This course is particularly suitable for those who are interested in working as data analysts or data scientists in the future.
- Single Honours Computing Project (CS4529)
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60 Credit Points
Consists of a supervised project which provides experience of investigating a real problem in computing science, or a computing application/technology. Learners will apply knowledge and skills gained earlier in their degree programme, and seek to go even further. Managing the project and presenting the results obtained are an integral part of the investigation.
Optional Courses
Plus 15 credits from courses of choice to make up 120 credit points
- Year 5
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Compulsory Courses
- Symbolic AI (CS502K)
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15 Credit Points
This course presents the fundamental techniques of Artificial Intelligence, used in system such as Google Maps, Siri, IBM Watson, as well as industrial automation systems, and which are core to emerging products such as self-driving vehicles. This course will equip the student to understand how such AI technologies operate, their implementation details, and how to use them effectively. This course therefore provides the building blocks necessary for understanding and using AI techniques and methodologies.
- Machine Learning (CS5062)
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15 Credit Points
This course will deliver the most sophisticated Machine Learning methodologies and algorithms which would be illustrated across a wide range of applications including but not limited to images, videos, health, time series data, language processing, etc. This course provides students with the Machine Learning principles for continuing learning and working in the area of Data Science and Artificial Intelligence.
- Evaluation of AI Systems (CS5063)
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15 Credit Points
One of the biggest challenges in Artificial Intelligence is evaluating how well AI systems work. This course will provide students of our MSc in AI with knowledge of core evaluation concepts, approaches, tools, techniques and technologies; we will also look at software testing of AI systems.
- Knowledge Representation and Reasoning (CS551J)
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15 Credit Points
Recent advances in AI have changed the perception of what machines can do, from on-line search to answering questions. An underlying feature of many AI systems concern how knowledge is acquired, represented, and reasoned with. Companies such as Google, IBM, and Facebook have been developing sophisticated tools for knowledge representation and reasoning. This module provides the theory and practice of knowledge representation and reasoning, also presenting cutting-edge technologies, libraries and tools. At the end of the course students will be able to design, implement and evaluate knowledge-intensive AI systems.
- Software Agents and Multi - Agent Systems (CS551K)
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15 Credit Points
The global autonomous systems market is expected to be valued at over £13 billion by 2025, involving both software systems and robots. Such autonomous systems act to achieve goals with no human intervention, and are already found in Tesla's self-driving cars, NASA space probes and systems such as Amazon's Echo. This course provides the student with a solid grounding in the theory and tools which underpin such systems, teaching them both how to develop such systems, and use them effectively as part of a larger product.
- Data Mining with Deep Learning (CS552J)
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15 Credit Points
This course aims to make students familiar with basic data mining and visualisation techniques and software tools. Students will learn how to analyse complex datasets by applying data pre-processing, exploration, clustering and classification, time series analysis, and many other techniques. This course will also cover text mining and qualitative modelling. Through this course students will be able to analyse real-world datasets in various domains and discover novel patterns from them. This course is particularly suitable for those who are interested in working as data analysts or data scientists in the future.
- Natural Language Generation (CS551H)
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15 Credit Points
The aim of the course is to introduce students who have some background in computing to (1) the varied aims for which Natural Language Generation (NLG) is pursued, (2) the main rule based and statistical methods that are used in NLG, and (3) some of the main NLG algorithms and systems. The course will cover NLG both as a theoretical enterprise (e.g., for constructing models of language production) and as practical language engineering, paying particular attention to the link between NLG and data science. Some programming experience is expected.
- MSc Project in Artificial Intelligence (CS5917)
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60 Credit Points
This course will provide students of our MSc in AI programme with the opportunity to develop their own AI research project, under the supervision of a member of staff. Typical projects include extending, improving or adapting existing AI theories or techniques to solve different problems, comparing competing techniques or tools to solve a particular problem, and so on. Students will improve their problem-solving and communication skills, as well as broaden, deepen and consolidate knowledge obtained in other components of the degree.
Optional Courses
Plus one of the following options:
- Applied Artificial Intelligence (CS5079)
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15 Credit Points
This course will allow students to use cutting-edge AI technologies to investigate the creation and application of AI systems. Such tools include deep learning libraries and simulation environments.
- AI and Data: Ethical and Legal Considerations (PH5065)
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15 Credit Points
This course will introduce and investigate a number of legal and ethical issues around the ethics of technology, particularly around the ethics of artificial intelligence. We will address questions such as the moral status of artificial agents; the difference, if any, between human rights and artificial rights, problems of data bias. We will also consider the question of responsibility in this arena and review regulatory frameworks. This course would be of interest to students from computer science, philosophy, law and health sciences.
We will endeavour to make all course options available. However, these may be subject to change - see our Student Terms and Conditions page.
How You'll Study
Learning Methods
- Group Projects
- Individual Projects
- Lectures
- Research
- Seminars
- Tutorials
- Workshops
Assessment Methods
Students are assessed by any combination of three assessment methods:
- coursework such as essays and reports completed throughout the course;
- practical assessments of the skills and competencies they learn on the course; and
- written examinations at the end of each course.
The exact mix of these methods differs between subject areas, years of study and individual courses.
Honours projects are typically assessed on the basis of a written dissertation.
Why Study Computing Science (Artificial Intelligence)?
- The MEng Computing Science (Artificial Intelligence) covers both the theoretical underpinning of AI as well as the technology, techniques, tools, software and methodologies used to apply these underlying theories to real-world problems.
- You will learn the key skills in the use of Symbolic AI, Machine Learning, Data Mining and Natural Language Processing in order to be able to engineer, develop and evaluate AI systems
- Your research project gives you the opportunity to further enhance your problem-solving and communication skills and apply the skills and knowledge you obtain throughout the programme.
- There has been a huge increase in demand for AI specialists across almost every industry, from energy and manufacturing to healthcare and cybercrime. This recent LinkedIn Jobs on the Rise report listed Machine Learning Engineer as one of the fastest-growing jobs in the UK.
- The University of Aberdeen has a strong history and worldwide reputation in computing science, in particular around Data Science, Natural Language Generation and Artificial Intelligence and is home to the research success of ARRIA NLG - the global leader in the field of natural language generation (NLG).
Entry Requirements
Qualifications
The information below is provided as a guide only and does not guarantee entry to the University of Aberdeen.
The information displayed in this section shows a shortened summary of our entry requirements. For more information, or for full entry requirements for Sciences degrees, see our detailed entry requirements section.
English Language Requirements
To study for an Undergraduate degree at the University of Aberdeen it is essential that you can speak, understand, read, and write English fluently. The minimum requirements for this degree are as follows:
IELTS Academic:
OVERALL - 6.0 with: Listening - 5.5; Reading - 5.5; Speaking - 5.5; Writing - 6.0
TOEFL iBT:
OVERALL - 78 with: Listening - 17; Reading - 18; Speaking - 20; Writing - 21
PTE Academic:
OVERALL - 59 with: Listening - 59; Reading - 59; Speaking - 59; Writing - 59
Cambridge English B2 First, C1 Advanced or C2 Proficiency:
OVERALL - 169 with: Listening - 162; Reading - 162; Speaking - 162; Writing - 169
Read more about specific English Language requirements here.
Fees and Funding
Please refer to our Tuition Fees page for fee information for this programme, or contact study@abdn.ac.uk.
Scholarships and Funding
Students from England, Wales and Northern Ireland, who pay tuition fees may be eligible for specific scholarships allowing them to receive additional funding. These are designed to provide assistance to help students support themselves during their time at Aberdeen.
Additional Fees
- In exceptional circumstances there may be additional fees associated with specialist courses, for example field trips. Any additional fees for a course can be found in our Catalogue of Courses.
- For more information about tuition fees for this programme, including payment plans and our refund policy, please visit our Tuition Fees page.
Our Funding Database
View all funding options in our Funding Database.
Careers
There are many opportunities at the University of Aberdeen to develop your knowledge, gain experience and build a competitive set of skills to enhance your employability. This is essential for your future career success. The Careers and Employability Service can help you to plan your career and support your choices throughout your time with us, from first to final year – and beyond.
- More information on employability at the University of Aberdeen
- More information on the Careers and Employability Service
Our Experts
Information About Staff Changes
You will be taught by a range of experts including professors, lecturers, teaching fellows and postgraduate tutors. However, these may be subject to change - see our Student Terms and Conditions page.
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Get in Touch
Contact Details
- Address
-
Student Recruitment & Admissions
University of Aberdeen
University Office
Regent Walk
Aberdeen
AB24 3FX