MSc in Artificial Intelligence
Belfast, United Kingdom
DURATION
1 Years
LANGUAGES
English
PACE
Full time, Part time
APPLICATION DEADLINE
Request application deadline
EARLIEST START DATE
16 Sep 2024
TUITION FEES
GBP 25,800 *
STUDY FORMAT
On-Campus
* for EU and international
Introduction
In the last decade, advances in Artificial Intelligence have made it at the forefront of technology, with many advances improving our daily lives.
Such is important that AI has become a national priority in many countries, including the UK, the US, China, and India.
As a result, there is a huge demand for specialist graduates with advanced AI knowledge and skills.
Studying MSc Artificial Intelligence at Queen’s provides you with the building blocks required for a career in the AI sector, as a researcher or an engineer.
Through a combination of lectures, tutorials, and practical learning you will investigate the fundamentals of AI and the latest AI technologies. By familiarising yourself with the main areas of AI that are already being used in industry you will be primed to push this learning even further. Each module acts as a building block that allows you to work towards a themed research project.
Course Structure
Students may enrol on a full-time (1 year) or part-time (3 years) basis. Individual modules may be studied as a short course. Full-time students typically complete three modules per semester. Part-time students typically complete one or two modules per semester.
The MSc is awarded to students who complete six taught modules (120 CATS points) and a 15,000 - 20,000 word research dissertation (60 CATS points ).
Exit qualifications are available: students may exit with a Postgraduate Diploma by completing 120 CATS points from taught modules or a Postgraduate Certificate by completing 60 CATS points from taught modules.
Duration
3 years Part Time/ 1 year Full Time
Admissions
Scholarships and Funding
Applicants are advised to explore fully the funding opportunities for studying in the UK, for example, international students may find funding is available from sources within their own countries.
The funding set out in this section includes funding available from the University and from some external sources. Information provided in this section is intended to highlight some sources of funding: it is not a comprehensive list of funding sources.
Applying for funding which is available from the University is part of an integrated, online, postgraduate admissions process. An offer of a place at Queen’s does not constitute an offer of financial support.
For 2023 entry, Faculties and Schools in the University will be setting their own deadlines for postgraduate applications for admissions, studentships and scholarships. Applicants who wish to apply for postgraduate funding available from the University for 2023 entry should refer to the relevant Faculty and School websites for information.
- The Department for the Economy will provide a tuition fee loan of up to £6,500 per NI / EU student for postgraduate study.
- A postgraduate loans system in the UK offers government-backed student loans of up to £11,836 for taught and research Masters courses in all subject areas.
Curriculum
AI for Health
This module will serve as a case study of AI applications. It will cover contemporary digital health topics such as precision medicine, diagnostics, medical imaging, and drug discovery. It will develop the ability to utilize AI principles and techniques to solve some health challenges, the ability to obtain relevant data from recognized repositories, the ability to utilize existing libraries and packages for analyzing and visualizing health data, and the transferable skills to apply AI to solve practical challenges.
Computer Vision
This module will cover deep neural networks (DNNs) and modern approaches to computer vision including DNN models for various computer vision tasks and current topics of computer vision. It will develop the ability to utilize DNN models to solve real-world computer vision challenges, the ability to obtain image/video data from recognized repositories, and the ability to utilize existing libraries and packages for implementing appropriate DNN models for a given computer vision task.
Course Details
The MSc in Artificial Intelligence is available in a full-time or a part-time option.
Full-time (1-year)
Part-time (2+ years): Part-time students are normally enrolled for two years.
Modules are regularly updated to reflect new developments in the dynamic field of Artificial Intelligence. Modules offered may be subject to change.
Foundations of AI
This module will cover the fundamental mathematics underlying AI including probability and statistics, calculus, algebra, and optimization. It will provide you with a sound understanding of the fundamentals; develop the ability to utilize them to understand and explain various AI techniques, and the ability to identify the most suitable modeling, optimization, factorization, and transformation approach for a given problem.
Knowledge Engineering
This module will cover classical and modern knowledge engineering techniques including logic, ontology, knowledge graph, and uncertainty reasoning. It will provide you with a systematic understanding of knowledge, principles, and procedures of knowledge engineering, develop your ability to utilize suitable knowledge-based methods to solve real-world problems, and ability to evaluate and compare the performance of knowledge-based solutions for a given problem.
Machine Learning
This module will cover different types of machine learning and various algorithms of each type. It will provide you with a systematic understanding of machine learning as a subject area, develop your ability to identify problems that can be solved using machine learning methods, apply suitable machine learning algorithms and software packages to solve real-world problems, evaluate and compare the performance of machine learning methods for a given problem, and to present and discuss the results of machine learning methods and propose appropriate improvements to methods.
Natural Language Processing
This module will mainly cover modern approaches to natural language processing (NLP), including various deep neural networks (DNNs) for NLP, and current topics of NLP.
It will develop the ability to utilize DNN models to solve real-world NLP challenges, the ability to obtain text/speech data from recognized repositories, the ability to utilize existing libraries and packages for developing NLP models, and an awareness of current developments, methods, and applications of NLP.
Themed Project
A themed project is a research project with an approved theme. Each theme may run for several years and is related to a strong area of research in the School. The topic of each project should be drawn from the following thematic areas of artificial intelligence (AI) covered by the Programme: machine learning (e.g., detection learning), knowledge engineering (e.g., clinical decision support systems, AI for education), computer vision (e.g., video search), natural language processing (e.g., question answering), and AI for health (e.g., medical image processing, biomarker discovery). Subject to approval by the Programme Committee, other themes not covered by the Programme may be included. These additional themes may be sponsored by a third party (e.g., a company) and sponsorship may be in the form of paying for the Levelling-Up Programme at the start of the Project. In exceptional cases, a project may have a topic outside these thematic areas.
Program Tuition Fee
Career Opportunities
Where Would You Like To Be In Five Years' Time
A thought leader in AI, showcasing technological advancements through research. Working for some of the largest companies on the planet. Or even advising government policy. The future is an exciting place, full of opportunity.