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An exciting two-year part-time programme to give current and potential AI engineers the skills, theory and recognition they need to develop in their role. Candidates can gain a full MSc degree in this specialist area through a mixed learning process with an emphasis on practical application in the workplace.
Programme Intake: September – There is an option to enter this Masters in January if you have already completed a Certificate in AI or if you satisfy suitable criteria. Express your interest to be notified about this option. Applicants eligible for ICTSkillnet funding must apply in the Summer for the Sept start. The deadline to apply for funding is 14th July 2023.
(M) Microcreds available:
Modules listed within the Programme Content with an (M) beside them are MicroCreds and can be taken independently.
The programme runs over 2 years with 5 teaching semesters and 1 research project semester and includes a Certificate in Artificial Intelligence awarded at level 8 of the National Framework of Qualifications at the end of the first semester.
Aimed at existing information technology professionals and those migrating from associated disciplines with the necessary computing and mathematics competencies, this programme will provide participants with robust knowledge and skills in the application of Artificial Intelligence and Machine Learning.
Participants must complete the preparatory Certificate in Artificial Intelligence to the equivalent of a 2nd class honours level to be eligible for entry to the Masters, regardless of their prior qualifications or experience. Successful completion of the preparatory Course will lead to the award of a Certificate in Artificial Intelligence by UL (Special Purpose Award, Level 8, 12 ECT credits). The 12 credits gained by completing the two Certificate modules will count towards the 90 credits of the MSc in Artificial Intelligence.
One distinguishing quality of this programme is that each and every module delivered on this programme was designed specifically for this programme. ICT Skillnet and our Industry Advisory Board provided extensive input to ensure content is relevant to industry, and online delivery is at the core of the pedagogical approach. As a result, all modules are structured around a number of so-called E-tivities. These allow you to explore relevant subjects in a self-driven manner, with expert support from module leaders and moderators who guide students in smaller groups. This type of learning has been demonstrated to be an extremely efficient method of learning.
The programme is delivered primarily via on-line lectures, supported with tutorials and assignments. Assessment is largely based on assignments and project work with a practical rather than theoretical focus.
Modules will be delivered with associated assessment of mastery so that semester by semester there is a confirmed and measurable achievement of learning objectives that can be transferred directly and immediately to the workplace.
Expected study time is 15-20 hours per week/module.
A major dissertation project will be selected and specified within the first year and completed throughout the second year.
MSc in Artificial Intelligence
Autumn (Certificate &MSc)
An introduction to the core mathematics and core programming skills required in machine learning. Using a number of E-tivities you will hone your Python coding skills as well as your knowledge and skills in Calculus, Linear Algebra and Probability Theory as the three core areas of mathematics that underpin machine learning.
Introduction To Deep Learning And Frameworks
A sneak preview of the exciting possibilities that modern machine learning offers, introducing you to the core methods used in machine learning and state-of-the-art networks, such as Convolutional Neural Networks.
An introduction to the core concepts in machine learning and familiarising you with the theory that underpins statistical machine learning. This module provides important insights into why and when machine learning is possible, and how to ensure the best performance possible.
An introduction to a large number of practical skills used in machine learning, including approaches to pre-processing data, using this data to train various machine learning algorithms, and methods to visualise the data and the performance of your machine learning models.
Note: summer module Advanced Seminars and Project Specification begins in Week 1 of the Spring semester.
Summer (May -Jun)
Advanced Topics Seminars and Project Specification
You will be introduced to a number of advanced topics through seminars (commencing in the Spring semester) to help you decide on your topic of interest for your project. You will also learn about the crucial research methods required to successfully conduct a Master’s level research project and write a literature review on the topic of your choice.
Risk, Ethics, Governance And Artificial Intelligence
A crucial element of your education as a responsible AI engineer, this module will introduce you to the risks and ethical issues associated with Artificial Intelligence.
In Year 2, students can choose to follow the Modern Machine Learning stream, the Natural Language Processing stream or the Computer Vision stream
||Modern Machine Learning||
Natural Language Processing
Machine Learning Applications
This module will introduce you to advanced machine learning models and applications, including Natural Language Processing and probabilistic approaches to machine learning.
Covers traditional methods of machine vision, as well as act as an introduction to the exciting area of deep learning, which has driven many of the most recent innovations in machine vision.
An introduction to the world of Natural Language Processing (NLP), this module covers the fundamentals of statistical NLP, and its techniques and applications with a foundational approach
This module introduces students to the fields of Information Retrieval, Information Extraction, and Semantic Web. The module will cover a blend of fundamental concepts and current tools, techniques, and technologies used in modern information retrieval systems.
Deep Learning for Computer Vision
In this module the application of deep learning to the key computer vision tasks of image classification, object detection, semantic segmentation and facial recognition is discussed in detail along with fundamental concepts in the design and structure of deep neural networks. Students gain a full understanding of how to design and build networks for their own applications.
Machine Vision and Image Processing
This module will introduce students to the principles of Machine Vision & Image Processing. Key topics such as linear image processing, feature detection and basic object detection are introduced with practical examples of these techniques.
Taking an in-depth look at deep learning theory and practice, you will learn about the most important deep neural network architectures as well as the most important deep learning frameworks. You will then apply your newfound knowledge to a number of sample applications.
Artificial Intelligence and Data Science Ecosystems: Theory And Practice
This module shows the two opposite sides of machine learning practice. On the one side, you will take a closer look at the algorithms that drive machine learning. On the other side, you will see how you can leverage these algorithms in AI workflows whilst minimising the coding effort through a model-driven design approach.
Advanced Natural Language Processing
This module covers advanced level topics in natural language processing, with a focus on deep learning-based approaches. These include text classification, synthetic parsing, part of speech tagging, named-entity recognition, coreference resolution, and machine translation.
Natural Language Understanding
This module introduces students to the field of Natural Language Understanding and related topics including sentiment analysis, relation extraction, natural language inference, semantic parsing, question answering, language generation, and conversational agents.
Geometric Computer Vision
Geometric computer vision is the process of determining the structure of the environment, the position, orientation and movement of the camera with respect to the environment, through the analysis of camera image streams. Students will gain a practical understanding of its use in mobile robotics, vehicle autonomy and augmented reality.
Intelligent Visual Computing & Applications
This module focuses on applications of Deep-learning to important Computer Vision applications including Facial Recognition and 3D reconstruction. The use of transformer networks to build state-of-the art computer vision system is also discussed.
The project which you have been working on throughout the summer of Year 1 and all of Year 2 is due in this semester. There are generally two options for submission: one at the start of the summer and one at the end of the summer, thus allowing you to finalise your project and dissertation during the summer of Year 2.
(M) A MicroCred is a single module within a larger further award (eg. Certificate, Diploma, Masters). By taking a micro-credential you may be eligible to apply for a credit exemption should you progress to study for a further award.
Category 1: The principal entry requirement is a Level 8 honours degree, at minimum second class honours (NFQ or other internationally recognised equivalent), in a relevant engineering, computing, mathematics, science or technology discipline.
Category 2: Applicants who possess a Level 8 Honours degree in other disciplines, which have a significant mathematics and computing element, will also be considered.
Category 3: Applicants who possess a Level 8 honours degree at minimum second class honours in a non-numerate discipline and have a minimum of three years experiential learning in an appropriate computing discipline may also be considered. A decision on the suitability and relevance of their experiential learning will be made by the University of Limerick.
Category 4: Applicants who do not meet any of the minimum educational requirements but have:
at least seven years work experience in a relevant computing or engineering environment
are in a senior or supervisory role in a company engaged in activities relevant to the subject matter of the programme; may be considered under the University of Limerick policy that allows for the Recognition of Prior Learning (both formal and informal/experiential learning), non-accredited personal and professional education; industry-accredited certifications; and work-based training.
What to include in your application:
Scanned original copies of your Award Certificates/Full Transcripts for examinations mentioned on your application form
For Irish/EU applicants– Award certificate with overall award or Final Transcript
Non- EU applicants - Award certificate with overall award and Final Transcript
Graduates of UL need only provide us with their UL Student ID number.
- If your qualifications have been obtained in a country where English is an official language this will suffice
If this is not available, the following additional documents must be provided:
• English translation of your qualification(s)/transcripts
• English language competency certificate
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A copy of passport for verification of full legal name.
Applicants in Categories 2 to 4 will need to upload a CV* and if applicable other relevant documentation to support their application, such professional certificates, supporting statements, etc.
Applicants in Category 4 will need to complete the RPL (Recognition of Prior Learning) form and submit it with their application.
*Please Note: Preferred CV format to have Educational detail before Employment detail
EU - €4950 per annum
Non- EU - €7350 per annum
(Certificate in AI: €1950)
Further information on fees and payment of fees is available from the Student Fees Office website. All fee related queries should be directed to the Student Fees Office (Phone: +353 61 213 007 or email email@example.com.)
Eligible candidates can avail of grant-aided fees from ICT Skillnet. For information, email firstname.lastname@example.org. The deadline to apply for this is 14th July 2023.