Artificial Intelligence - PgCert

2024/25 Part-time Postgraduate course

Award:

Postgraduate Certificate

Faculty:

Faculty of Computing, Engineering and the Built Environment

School:

School of Computing

Campus:

Belfast campus

Start dates:

September 2024

January 2025

Overview

Developing new skills in the emerging field of Artificial Intelligence

Summary

Artificial Intelligence is a core discipline within Computer Science; however, it has applications in almost every industry sector including, but not limited to, Health, Financial Technology, Advanced Manufacturing, Media, Energy, Civic Society and Public Policy.

The UK will need a larger workforce with deep AI expertise, and more development of lower level skills to work within the domain. This specialist 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 course will cover all core areas including preparing participants for a career with knowledge and problem-solving skills in AI and with an appreciation of how it can be used in a range of applications.

The course content has been informed by internationally leading research being conducted in the School and by our strong industry partnerships.

There are also opportunities for graduates from the PgCert Artificial Intelligence to embark on further research by enrolling for PhD study affiliated with the research centres within the School of Computing.

We’d love to hear from you!

We know that choosing to study at university is a big decision, and you may not always be able to find the information you need online.

Please contact Ulster University with any queries or questions you might have about:

  • Course specific information
  • Fees and Finance
  • Admissions

For any queries regarding getting help with your application, please select Admissions in the drop down below.

For queries related to course content, including modules and placements, please select Course specific information.

We look forward to hearing from you.

About this course

About

The Postgraduate Certificate award consists of three compulsory taught modules (totaling 60 credits)

Data Science and Machine Learning

This module provides an overview of Data Science process/pipeline. It provides systematic understanding of mathematical and statistical knowledge for explorable data analysis (EDA) and to understand the foundations of supervised and unsupervised machine learning algorithms, and with the practical programming skills to apply them to real world datasets. The module discusses the constraints that needs to be considered when designing, implementing, evaluating and visualising solutions to real-world complex problems.

Knowledge Engineering

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, automated reasoning and formal verification, and semantic web. Students will gain deep understanding of key concepts and principles, and gain practical skills in critically evaluating and effectively building knowledge-based applications.

Emerging and Advanced Topics in AI

This module will cover cutting-edge topics in the field of artificial intelligence, including recent advances in AI theory, algorithms and applications, as well as issues such as privacy, fairness and ethics in artificial intelligence. In doing so a number of examples of advanced AI systems and applications are reviewed. Students will gain deep understanding of key concepts, principles, and challenges, and gain practical skills in critically evaluating and effectively building AI-based applications. The module will also help students develop their skills in independent learning, research skills, writing, as well as practical skills in using software to reproduce results from the literature.

Attendance

Typically 5-10 timetabled on campus hours per week Monday – Friday including lectures, tutorials and practicals in the computer labs for the taught components of the course.

Start dates

  • September 2024
  • January 2025

Teaching, Learning and Assessment

Teaching is delivered through lectures, directed tutorials, seminars, and practical sessions, some of which are by industry professionals / researchers.

The course is assessed by 100% coursework.

Teaching, learning and assessment

The content for each course is summarised on the relevant course page, along with an overview of the modules that make up the course.

Each course is approved by the University and meets the expectations of:

Attendance and Independent Study

As part of your course induction, you will be provided with details of the organisation and management of the course, including attendance and assessment requirements - usually in the form of a timetable. For full-time courses, the precise timetable for each semester is not confirmed until close to the start date and may be subject to some change in the early weeks as all courses settle into their planned patterns. For part-time courses which require attendance on particular days and times, an expectation of the days and periods of attendance will be included in the letter of offer. A course handbook is also made available.

Courses comprise modules for which the notional effort involved is indicated by its credit rating. Each credit point represents 10 hours of student effort. Undergraduate courses typically contain 10, 20, or 40 credit modules (more usually 20) and postgraduate courses typically 15 or 30 credit modules.

The normal study load expectation for an undergraduate full-time course of study in the standard academic year is 120 credit points. This amounts to around 36-42 hours of expected teaching and learning per week, inclusive of attendance requirements for lectures, seminars, tutorials, practical work, fieldwork or other scheduled classes, private study, and assessment. Teaching and learning activities will be in-person and/or online depending on the nature of the course. Part-time study load is the same as full-time pro-rata, with each credit point representing 10 hours of student effort.

Postgraduate Master’s courses typically comprise 180 credits, taken in three semesters when studied full-time. A Postgraduate Certificate (PGCert) comprises 60 credits and can usually be completed on a part-time basis in one year. A 120-credit Postgraduate Diploma (PGDip) can usually be completed on a part-time basis in two years.

Class contact times vary by course and type of module. Typically, for a module predominantly delivered through lectures you can expect at least 3 contact hours per week (lectures/seminars/tutorials). Laboratory classes often require a greater intensity of attendance in blocks. Some modules may combine lecture and laboratory. The precise model will depend on the course you apply for and may be subject to change from year to year for quality or enhancement reasons. Prospective students will be consulted about any significant changes.

Assessment

Assessment methods vary and are defined explicitly in each module. Assessment can be a combination of examination and coursework but may also be only one of these methods. Assessment is designed to assess your achievement of the module’s stated learning outcomes.  You can expect to receive timely feedback on all coursework assessments. This feedback may be issued individually and/or issued to the group and you will be encouraged to act on this feedback for your own development.

Coursework can take many forms, for example: essay, report, seminar paper, test, presentation, dissertation, design, artefacts, portfolio, journal, group work. The precise form and combination of assessment will depend on the course you apply for and the module. Details will be made available in advance through induction, the course handbook, the module specification, the assessment timetable and the assessment brief. The details are subject to change from year to year for quality or enhancement reasons. You will be consulted about any significant changes.

Normally, a module will have 4 learning outcomes, and no more than 2 items of assessment. An item of assessment can comprise more than one task. The notional workload and the equivalence across types of assessment is standardised. The module pass mark for undergraduate courses is 40%. The module pass mark for postgraduate courses is 50%.

Calculation of the Final Award

The class of Honours awarded in Bachelor’s degrees is usually determined by calculation of an aggregate mark based on performance across the modules at Levels 5 and 6, (which correspond to the second and third year of full-time attendance).

Level 6 modules contribute 70% of the aggregate mark and Level 5 contributes 30% to the calculation of the class of the award. Classification of integrated Master’s degrees with Honours include a Level 7 component. The calculation in this case is: 50% Level 7, 30% Level 6, 20% Level 5. At least half the Level 5 modules must be studied at the University for Level 5 to be included in the calculation of the class.

All other qualifications have an overall grade determined by results in modules from the final level of study. In Master’s degrees of more than 200 credit points the final 120 points usually determine the overall grading.

Figures correct for academic year 2022-2023.

Academic profile

Academic staff in the School of Computing are qualified to teach in higher education with most of them holding at least a Postgraduate Certificate in Higher Education Practice. The majority of academic staff in the School (89%) are accredited fellows of the Higher Education Academy (HEA) or above. Within the School of Computing courses are taught by staff who are Professors (22%), Readers/Senior Lecturers (28%) and Lecturers (50%)

The University employs over 1,000 suitably qualified and experienced academic staff - 60% have PhDs in their subject field and many have professional body recognition.

Courses are taught by staff who are Professors (19%), Readers, Senior Lecturers (22%) or Lecturers (57%).

We require most academic staff to be qualified to teach in higher education: 82% hold either Postgraduate Certificates in Higher Education Practice or higher. Most academic and learning support staff (85%) are recognised as fellows of the Higher Education Academy (HEA) by Advance HE - the university sector professional body for teaching and learning. Many academic and technical staff hold other professional body designations related to their subject or scholarly practice.

The profiles of many academic staff can be found on the University’s departmental websites and give a detailed insight into the range of staffing and expertise.  The precise staffing for a course will depend on the department(s) involved and the availability and management of staff.  This is subject to change annually and is confirmed in the timetable issued at the start of the course.

Occasionally, teaching may be supplemented by suitably qualified part-time staff (usually qualified researchers) and specialist guest lecturers. In these cases, all staff are inducted, mostly through our staff development programme ‘First Steps to Teaching’. In some cases, usually for provision in one of our out-centres, Recognised University Teachers are involved, supported by the University in suitable professional development for teaching.

Figures correct for academic year 2022-2023.

Belfast campus

Belfast Campus Location

The Belfast campus is situated in the artistic and cultural centre of the city, the Cathedral Quarter.

Find out more about our Belfast Campus.

Campus Address

Ulster University,
2-24 York Street,
Belfast
BT15 1AP

T: 02870 123 456

Accommodation

High quality apartment living in Belfast city centre adjacent to the university campus.

Find out more - information about accommodation  


Student Wellbeing

At Student Wellbeing we provide many services to help students through their time at Ulster University.

Find out more - information about student wellbeing  

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

Data Science and Machine Learning

Year: 1

This module provides an overview of Data Science process/pipeline. It provides systematic understanding of mathematical and statistical knowledge for explorable data analysis (EDA) and to understand the foundations of supervised and unsupervised machine learning algorithms, and with the practical programming skills to apply them to real world datasets. The module discusses the constraints that needs to be considered when designing, implementing, evaluating and visualising solutions to real-world complex problems.

Knowledge Engineering

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, automated reasoning and formal verification, and semantic web. Students will gain deep understanding of key concepts and principles, and gain practical skills in critically evaluating and effectively building knowledge-based applications.

Emerging and Advanced Topics in AI

Year: 1

This module will cover emerging topics in the field of artificial intelligence, including recent advances in AI theory, algorithms and applications, as well as issues such as privacy, fairness and ethics in artificial intelligence. In doing so a number of examples of advanced AI systems and applications are reviewed. Students will gain deep understanding of key concepts, principles, and challenges, and gain practical skills in critically evaluating and effectively building AI-based applications. The module will also help students develop their skills in independent learning, research skills, as well as practical skills.

Standard 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.

Entry Requirements

Applicants must:

(a) have gained

(i) a second class honours degree or better, in the subject areas of Computer Science, Software Engineering, Electronic Engineering, Electrical Engineering, Mathematics, Physics or closely related discipline, 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 excluding Conversion courses; and the qualification must be in the subject areas of Computer Science, Software Engineering, Electronic Engineering, Electrical Engineering, Mathematics, Physics or closely related discipline.

and

(b) provide evidence of competence in written and spoken English (GCSE grade C or equivalent). For applicants whose first language is not English the minimum English language requirement is an Academic IELTS 6.0 with no band score less than 5.5, Trinity ISE: Pass at level III or equivalent English language tests comparable to IELTS equivalent score.

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

The UK will need a larger workforce with deep AI expertise, and more development of lower level skills to work within the domain. On successful completion of the programme, students can continue on to our full MSc in Artificial Intelligence.

Apply

Start dates

  • September 2024
  • January 2025

Fees and funding

2024/25 Fees

Our postgraduate fees are subject to annual increase and are currently under review.

See our tuition fees page for the current fees for 2023/24 entry.

Additional mandatory costs

It is important to remember that costs associated with accommodation, travel (including car parking charges) and normal living will need to be covered in addition to tuition fees.

Where a course has additional mandatory expenses (in addition to tuition fees) we make every effort to highlight them above. We aim to provide students with the learning materials needed to support their studies. Our libraries are a valuable resource with an extensive collection of books and journals, as well as first-class facilities and IT equipment. Computer suites and free Wi-Fi are also available on each of the campuses.

There are additional fees for graduation ceremonies, examination resits and library fines.

Students choosing a period of paid work placement or study abroad as a part of their course should be aware that there may be additional travel and living costs, as well as tuition fees.

See the tuition fees on our student guide for most up to date costs.

Contact

We’d love to hear from you!

We know that choosing to study at university is a big decision, and you may not always be able to find the information you need online.

Please contact Ulster University with any queries or questions you might have about:

  • Course specific information
  • Fees and Finance
  • Admissions

For any queries regarding getting help with your application, please select Admissions in the drop down below.

For queries related to course content, including modules and placements, please select Course specific information.

We look forward to hearing from you.


For more information visit

Disclaimer

  1. 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.
  1. 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.
  1. 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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