Course Code: MSARINTPAD/MSARINTPBD
Available: Part- Time
Duration: 2 Years Part-time
Award: Masters (MSc)
Qualification: Level 9 Masters (MSc)
Faculty: Science and Engineering
Course Type: Taught Professional/Flexible
Fees: For Information on Fees, see section below.
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.
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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.
Certificate in Artificial Intelligence
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.
A major dissertation project will be selected and specified within the first year and completed throughout the second year.
Msc in Artificial Intelligence
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.
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.
You will be introduced to a number of advanced topics through seminars (some will take place during the previous 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.
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.
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.
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.
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.
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.
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