Creating the next generation of high quality professionals for the AI industry.
The MSc Artificial Intelligence is a specialist programme that has the core aim of preparing students with skills in AI that are in high demand nationally and internationally. Graduates are placed to progress into a wide variety of careers, across a range of industrial settings and application domains. There are also opportunities for its graduates to embark upon PhD research studies.
Topics include Deep Learning, Big Data & Infrastructure, Machine Learning, Automatic Computing & Robotics, Statistical Modelling & Data Mining and Knowledge Engineering.
The course content has been informed by internationally leading research being conducted by the School of Computing. The delivery of the course is supported by a large-scale pervasive and mobile computing environment, a suite of contemporary sensing technologies and rapid prototyping facilities.
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About this course
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This programme has been developed in response to evidence of demand from industry and business for upskilling of staff in the area of AI and addresses a clear gap in the marketplace for postgraduate study. The UK will need a larger workforce with deep AI expertise, and more development of specialist skills to work within the domain.
The MSc in Artificial Intelligence will strive to address the growing demands being placed on the sector by training computing professionals to follow a career either in industry or academia where they can apply leading edge AI skills across a range of application domains.
The full-time provision offers two points of entry in each academic year: September and January. For the September intake, the degree will normally be completed in three semesters across a single academic year. For the January intake, the degree will normally be completed in three semesters but across two academic years.
- January 2020
Teaching, Learning and Assessment
Teaching is delivered through a combination of lectures, directed tutorials, seminars and practical sessions. Support is also provided for project preparation and implementation.
The course is assessed by coursework.
Ulster University academics are actively involved in both research and teaching and this ensures that the developments accrued through research can feed into the teaching of students. A high percentage of staff are members of the Higher Education Academy, and all staff are expected to have a Postgraduate Certificate in University Teaching or equivalent. All Computing and Engineering courses are subject to periodic Faculty Review and University Revalidation.
Here is a guide to the subjects studied on this course.
Courses are continually reviewed to take advantage of new teaching approaches and developments in research, industry and the professions. Please be aware that modules may change for your year of entry. The exact modules available and their order may vary depending on course updates, staff availability, timetabling and student demand. Please contact the course team for the most up to date module list.
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Big Data & Infrastructure
Within this module a variety of database and data storage paradigms will be explored, ranging from more traditional relational systems to NoSql and object stores, time series databases, semantic store and graph stores.
Consideration will be given to big data and the problem with storing and querying high volumes of highly variable data which is stored and processed at a high speed. The cloud computing paradigm will also be introduced and how to avail of its power and resources.
The core concepts of distributed computing will be examined in the context of Hadoop. Students will be taught, practically and theoretically, about the components of Hadoop, workflows, functional programming concepts, use of MapReduce, Spark, Pig, Hive and Sqoop.
Statistical Modelling & Data Mining
This module first provides a systematic understanding of probability and statistics. It then provides an in-depth analysis of the statistical modelling process and how to answer hypothesised questions. Next, the module provides a synthesis of the concepts of data mining and methods of exploring data. The content will be delivered and experienced through lectures, seminars and practical exercises using tools, such as Python, R and Weka. Online tools, such as Blackboard will be used to facilitate blended learning approach. On completing this module, students will be able to compute conditional probabilities and use null hypothesis significance testing to test the significance of results and understand and compute statistical measures such as the p-value for these tests. Students will apply, evaluate and critically appraise this knowledge in a range of complex real-world contexts.
The aim of the project is to allow the student to demonstrate their ability in undertaking an independent research project for developing theoretical perspectives, addressing research questions using data, or analysing and developing real-world solutions. They will be expected to utilise appropriate methodologies and demonstrate the skills gained earlier in the course when implementing the project.
As part of the project development activity, they will be required to extract and demonstrate knowledge from the literature in an analytic manner and develop ideas and appropriate hardware and software implementations. This may involve the development of a hardware sensor component or may access existing hardware to develop new/ novel software processing or data analytics. This will typically be followed by a structured analysis of needs for a realistic application or actual organisation and identification and application of tools/techniques required to deliver a well-formed solution. Through the project, the student will develop capabilities to analyse cases studies related to IoT/ Artificial Intelligence and its application in a range of domains including transport, environment, health and commerce. The project may further create improvement plans and recommendations for future implementation based on the tools/technologies experienced during the programme of study.
In summary, the Masters Project represents a piece of work performed by the student under suitable staff supervision which draws both from the practical and creative nature of a problem-solving project and the traditional, scholarly exposition of an area of study. The content of the work must be original and contain a critical appraisal of the subject area.
Knowledge Engineering & Computational Creativity
This module will cover modern topics in a classical field of artificial intelligence, including knowledge representation and reasoning (deductive and inductive), and their effective utilisation in e.g. decision making, semantic web and computational creativity. Students will gain deep understanding of key concepts and principles, and gain practical skills in critically evaluating and effectively building knowledge-based applications.
Autonomic Computing & Robotics
This module focuses on the development of self-managing systems, inspired by the human body's autonomic nervous system. The ANS is that part of the nervous system that manages body functions such as blood circulation, intestinal activity, and hormonal secretion and production, all without conscious effort. The desire of Autonomic Computing and Robotics is to bring a similar self-managing level of capability to systems, including robots, and thus free up the human user for higher-level concerns.
This module will provide students with the mathematical and statistical knowledge to understand the foundations of common supervised and unsupervised machine learning algorithms, and with the practical programming skills to apply them to real world data-sets. State-of-the art methods including probabilistic programming and explainable AI will also be introduced.
Deep Learning and Its Application
The module introduces the fundamental concepts of deep learning, neural networks as well as the theory associated with the development of successful deep learning algorithms. Students will learn state of the art convolutional neural networks, recurrent neural networks, loss functions and optimization process along with development tools, and apply them to the development of solutions for deep learning application domains (i.e. Computer Vision, Natural Language Processing, etc.)
We recognise a range of qualifications for admission to our courses. In addition to the specific entry conditions for this course you must also meet the University’s General Entrance Requirements.
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(a) have gained
(i) a second class lower division honours degree or better, in the subject areas of computing, engineering or cognate area from a university of the United Kingdom or the Republic of Ireland, or from a recognised national awarding body, or from an institution of another country which has been recognised as being of an equivalent standard; or
(ii) an equivalent standard (normally 50%) in a Graduate Diploma, Graduate Certificate, Postgraduate Certificate or Postgraduate Diploma or an approved alternative qualification; and the qualification must be in the subject areas of computing, engineering or related discipline
(b) provide evidence of competence in written and spoken English (GCSE grade C or equivalent).
In exceptional circumstances, as an alternative to (a) (i) or (a) (ii) and/or (b), where an individual has substantial and significant experiential learning, a portfolio of written evidence demonstrating the meeting of graduate qualities (including subject-specific outcomes, as determined by the Course Committee) may be considered as an alternative entrance route. Evidence used to demonstrate graduate qualities may not be used for exemption against modules within the programme.
English Language Requirements
English language requirements for international applicants
The minimum requirement for this course is Academic IELTS 6.0 with no band score less than 5.5. Trinity ISE: Pass at level III also meets this requirement for Tier 4 visa purposes.
Ulster recognises a number of other English language tests and comparable IELTS equivalent scores.
Exemptions and transferability
The entry requirements facilitate accreditation of prior learning.
Careers & opportunities
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Artificial Intelligence is expected to have a significant impact on industry and society, and more and more organisations are starting to embrace AI in their operations. The MSc in Artificial Intelligence aims to prepare students for an industrial career with knowledge and problemsolving skills in AI and with an appreciation of how it can be used in a range of applications, as well as an academic career with knowledge and research skills ready for higher postgraduate research studies.
Work placement / study abroad
Fees and funding
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Scholarships, awards and prizes
Additional mandatory costs
For course specific enquiries:
Dr Shuai Zhang
MSc Course Director
For more information visit
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