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Overview

Creating the next generation of high quality professionals for the AI industry.

Summary

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

In this section

About

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.

Attendance

The part-time provision offers two points of entry in each academic year: September and January. For both points of entry, the degree will normally be completed in six semesters across three academic years.

Start dates

  • January 2020
How to apply

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.

Academic profile

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.

Modules

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.

In this section

Year one

Big Data & Infrastructure

Year: 1

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

Year: 1

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.

Knowledge Engineering & Computational Creativity

Year: 1

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.

Machine Learning

Year: 1

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.

Year two

Autonomic Computing & Robotics

Year: 2

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.

Deep Learning and Its Application

Year: 2

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.)

Year three

Masters Project

Year: 3

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.

Entry conditions

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.

In this section

Entry Requirements

Applicants must:

(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

and

(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

In this section

Career options

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

N/A

Apply

How to apply Request a prospectus

Applications to our postgraduate courses are made through the University’s online application system.

Start dates

  • January 2020

Fees and funding

In this section

Scholarships, awards and prizes

N/A

Additional mandatory costs

None

Contact

For course specific enquiries:
Dr Shuai Zhang

MSc Course Director

School of Computing
T: +44 28 90366367
E: s.zhang@ulster.ac.uk

For more information visit

Faculty of Computing, Engineering and the Built Environment

School of Computing

Disclaimer

  1. The University endeavours to deliver courses and programmes of study in accordance with the description set out in this prospectus. The University’s prospectus is produced at the earliest possible date in order to provide maximum assistance to individuals considering applying for a course of study offered by the University. The University makes every effort to ensure that the information contained in the prospectus is accurate but it is possible that some changes will occur between the date of printing and the start of the academic year to which it relates. Please note that the University’s website is the most up-to-date source of information regarding courses and facilities and we strongly recommend that you always visit the website before making any commitments.
  2. Although reasonable steps are taken to provide the programmes and services described, the University cannot guarantee the provision of any course or facility and the University may make variations to the contents or methods of delivery of courses, discontinue, merge or combine courses and introduce new courses if such action is reasonably considered to be necessary by the University. Such circumstances include (but are not limited to) industrial action, lack of demand, departure of key staff, changes in legislation or government policy including changes, if any, resulting from the UK departing the European Union, withdrawal or reduction of funding or other circumstances beyond the University’s reasonable control.
  3. If the University discontinues any courses, it will use its best endeavours to provide a suitable alternative course. In addition, courses may change during the course of study and in such circumstances the University will normally undertake a consultation process prior to any such changes being introduced and seek to ensure that no student is unreasonably prejudiced as a consequence of any such change.
  4. The University does not accept responsibility (other than through the negligence of the University, its staff or agents), for the consequences of any modification or cancellation of any course, or part of a course, offered by the University but will take into consideration the effects on individual students and seek to minimise the impact of such effects where reasonably practicable.
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