Introduction
Artificial Intelligence (AI) forms part of many digital systems. No longer is AI seen as a special feature within software, but as an important development expected in modern systems. From word-processing applications to gaming, and from robots to the Internet of Things, AI tends to be responsible for controlling the underlying behaviour of systems. Such trends are forecast to grow further.
This programme is studied on campus.
Cloud-based neural networks will power 40% of mobile interactions between virtual personal assistants and people by 2020.
It is estimated that 85% of all customer interactions won't require human customer service reps by the end of this decade.
Such is the importance of AI, that the field is estimated to grow beyond £4 billion by 2020.
AI has entered an exciting new phase, in which problems once thought too complex for computers to solve are now tackled with considerable success. Examples of such problems are autonomous vehicles, translation of text, speech and photo recognition, and playing games. In studies, this has led to 80% of executives reporting that AI boosts worker performance and creates new jobs.
This MSc programme provides students with in-depth knowledge of recent advances in AI such as data and text mining, efficient reasoning, natural language generation, information visualisation and communication, and distributed AI systems. The programme is centred on practical “hands-on” learning, exploring underpinning theories in combination with the use of techniques, tools, software and methodologies with the opportunities, where possible, to work with international experts and companies at the cutting edge of the field.
Key Programme Information
At a Glance
- Learning Mode
- On Campus Learning
- Degree Qualification
- MSc
- Duration
- 12 months
- Study Mode
- Full Time
- Start Month
- September
What You'll Study
- Semester 1
-
Semester 1
Compulsory Courses
- Foundations of AI (CS5060) - 15 Credit Points
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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.
- Engineering AI Systems (CS5061) - 15 Credit Points
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Artificial intelligence has helped solve complex practical problems such as driving a car, translating text from/to different languages, understanding and answering questions, and playing games such as chess and Go. This course will provide students of our MSc in AI with skills to help them engineer AI systems, equipping them with solid programming skills, and using state-of-the-practice languages, tools and technologies
- Machine Learning (CS5062) - 15 Credit Points
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This course presents the fundamental as well as the most popular Machine Learning theories and algorithms, used in a wide range of applications such as classification, prediction, regression, and those are core to the design of for instance computer Go player AlphaGo. This course provides the building blocks for understanding and using Machine Learning techniques and methodologies and prepares students to work in data science and general AI systems.
- Evaluation of AI Systems (CS5063) - 15 Credit Points
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How do we assess whether an AI system works and is effective? Indeed, what does it mean for an AI system to be effective? In this course, we will look at different ways of evaluating AI systems, including performance on benchmark data sets, usefulness at helping users achieve a task, and subjective opinions (ie, do people like the system). Much of the course is devoted to statistics (including the R programming language), experimental design, and ethical issues. In practical and assessment work, students will evaluate deployed AI systems, and also critique evaluations in published AI research papers.
- Semester 2
-
Semester 2
In the second half-session, students develop advanced knowledge and skills in AI in order to prepare them for working with clients over the summer. This session also helps them to decide which areas of AI they wish to explore during their summer project whilst raising awareness of career opportunities in the area.
Compulsory Courses
- Data Mining and Visualisation (CS551G) - 15 Credit Points
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This course will provide students of our MSc in AI with knowledge of core data mining and visualisation approaches, tools, techniques and technologies. The students will be enhanced with data science skills for their future career.
- Natural Language Generation (CS551H) - 15 Credit Points
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Artificial intelligence has helped solve complex practical problems such as driving a car, translating text from/to different languages, understanding and answering questions, and playing games such as chess and Go. This course will provide students of our MSc in AI with knowledge of core natural language generation concepts, approaches, tools, techniques and technologies.
- Knowledge Representation and Reasoning (CS551J) - 15 Credit Points
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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) - 15 Credit Points
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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.
- Semester 3
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Semester 3
Compulsory Courses
- MSc Project in Artificial Intelligence (CS5917) - 60 Credit Points
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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.
Course Availability
We will endeavour to make all course options available; however, these may be subject to timetabling and other constraints. Please see our InfoHub pages for further information.
How You'll Study
Learning Methods
- Group Projects
- Individual Projects
- Lectures
- Peer Learning
- Research
- Tutorials
- Workshops
Why Study Artificial Intelligence?
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.
Home to the research success of ARRIA NLG, the University has been ahead of the game in computing science research and teaching for years. Find out more about this hugely successful spin-out company and how it impacts on Artificial Intelligence -
http://blog.arria.com/what-does-it-take-to-build-a-machine-learning-capacity-less-than-you-think
Entry Requirements
Qualifications
The information below is provided as a guide only and does not guarantee entry to the University of Aberdeen.
Our minimum entry requirement for this programme is a Computing Science degree at 2:2 (lower second class) UK Honours level (or an Honours degree from a non-UK institution which is judged by the University to be of equivalent worth).
Key subjects you must have covered: Java, C, C++, Algorithms problem-solving and Data Structures.
Applicants with a 2:2 or equivalent in Engineering (e.g. Electronic/Electrical Engineering) will be reviewed by the selectors for suitability on a case-by-case basis.
Please enter your country to view country-specific entry requirements.
English Language Requirements
To study for a Postgraduate Taught 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 - 6.5 with: Listening - 5.5; Reading - 5.5; Speaking - 5.5; Writing - 6.0
TOEFL iBT:
OVERALL - 90 with: Listening - 17; Reading - 18; Speaking - 20; Writing - 21
PTE Academic:
OVERALL - 62 with: Listening - 51; Reading - 51; Speaking - 51; Writing - 54
Cambridge English Advanced & Proficiency:
OVERALL - 176 with: Listening - 162; Reading - 162; Speaking - 162; Writing - 169
Read more about specific English Language requirements here.
Document Requirements
You will be required to supply the following documentation with your application as proof you meet the entry requirements of this degree programme. If you have not yet completed your current programme of study, then you can still apply and you can provide your Degree Certificate at a later date.
- CV
- an up-to-date CV/Resumé
- Degree Certificate
- a degree certificate showing your qualifications
- Degree Transcript
- a full transcript showing all the subjects you studied and the marks you have achieved in your degree(s) (original & official English translation)
- Personal Statement
- a detailed personal statement explaining your motivation for this particular programme
International Applicants
Fees and Funding
You will be classified as one of the fee categories below.
| Fee category | Cost |
|---|---|
| Home / EU / RUK Students | £6,700 |
| Tuition Fees for 2019/20 Academic Year | |
| International Students | £15,900 |
| Tuition Fees for 2019/20 Academic Year |
International non-EU Applicants
Further Information about tuition fees and the cost of living in 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 InfoHub Tuition Fees page.
Funding Opportunities
Data Lab scholarships are available to Scottish and EU students. There are a limited number of scholarships available so it is recommended that you apply early. Further information can be found on the Data Lab scholarship page.
The SFC Postgraduate tuition fee scholarship may be available for those classified as Home/EU fee status students for this programme. Visit the scholarship page for more information.
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 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 Service
Our Experts
- Other Expert
- Dr Nigel Beacham
- Programme Coordinator
- Dr Wei Pang
Information About Staff Changes
You will be taught by a range of experts including professors, lecturers, teaching fellows and postgraduate tutors. Staff changes will occur from time to time; please see our InfoHub pages for further information.
Get in Touch
Contact Details
- Address
-
Student Recruitment & Admissions Service
University of Aberdeen
University Office
Regent Walk
Aberdeen
AB24 3FX