Developing new skills in the emerging field of Artificial Intelligence
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 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:
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.
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The Postgraduate Certificate award consists of three compulsory taught modules (totaling 60 credits)
Machine Learning (20 credits)
Machine learning is the branch of artificial intelligence concerned with algorithms and statistical models that use data for various tasks.
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 datasets. State-of-the art methods including probabilistic programming and explainable AI will also be introduced.
Big Data & Infrastructure (20 credits)
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 (20 credits)
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.
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.
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.
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 (83%) are accredited fellows of the Higher Education Academy (HEA). Within the School of Computing courses are taught by staff who are Professors (20%), Readers/Senior Lecturers (32%) and Lecturers (48%)
The University employs over 1,000 suitably qualified and experienced academic staff - 59% have PhDs in their subject field and many have professional body recognition.
Courses are taught by staff who are Professors (25%), Readers, Senior Lecturers (20%) or Lecturers (55%).
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 staff (81%) are accredited fellows of the Higher Education Academy (HEA) by Advanced 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 2021-2022.
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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: 1
Status: C
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 a data lake. Students will be taught, practically and theoretically, about the components of Data lakes, workflows, functional programming concepts, use of MapReduce, Spark, Pig, and Hive
Year: 1
Status: C
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.
Year: 1
Status: C
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.
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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Applicants must have A or B:
A - an Honours or non-Honours degree,in the subject area of computing, engineering, mathematics or 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 is recognised as being of an equivalent standard.
B -an equivalent standard in a Graduate Certificate or Graduate Diploma or an approved alternative qualification; and the qualification must be in the subject areas of computing or related discipline
Competence in written and spoken English
Applicants mush provide evidence of competence in written and spoken English (GCSE grade C or equivalent).
Exceptional circumstances
In exceptional circumstances, as an alternative to above, 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 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.
The entry requirements facilitate accreditation of prior learning.
In this section
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.
The price of your overall programme will be determined by the number of credit points that you initiate in the relevant academic year.
For modules commenced in the academic year 2023/24, the following fees apply:
| Credit Points | NI/ROI/GB Cost | International Cost |
|---|---|---|
| 5 | £186.65 | £440 |
| 10 | £373.30 | £880 |
| 15 | £559.95 | £1,320 |
| 20 | £746.60 | £1,760 |
| 30 | £1,119.90 | £2,640 |
| 60 | £2,239.80 | £5,280 |
| 120 | £4,479.60 | £10,560 |
| 180 | £6,719.40 | £15,840 |
NB: A standard full-time PGCert is equivalent to 60 credit points per year. A standard full-time PGDip is equivalent to 120 credit points per year.
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.
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:
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.
