Ignacio Castiñeiras
Lecturer, Munster Technological University
David Murphy
Lecturer, Munster Technological University
A leading technological university is seeking to alleviate a skills shortage within the field of artificial intelligence (AI) with its flexible and hands-on approach to learning through an MSc course.
As the field of artificial intelligence continues to evolve at a pace, so too are the number of companies either already using it or looking into how their businesses can benefit from its implementation.
This exponential growth, however, is currently somewhat stymied by a critical skills shortage and, therefore, work opportunities for post-graduates skilled in AI application have never been greater. Munster Technological University (MTU) is working hard to address this issue and has already seen 200-plus students graduate from its MSc in Artificial Intelligence course.
Expert lecturers lead learning
Coordinators of the on-campus and online course are lecturers Ignacio Castiñeiras and David Murphy, whose courses aim to meet this skills gap.
Castiñeiras has a PhD in Computer Science and is an expert in real-life constraint satisfaction and optimisation problems, while Murphy has a background in electrical engineering and teaches robotics.
“In the field of AI, we try to optimise the resources that a company has, so we know what the demands and resources are — and how to apply them in the most optimal way,” explains Castiñeiras.
Work opportunities for
post-graduates skilled
in AI application have
never been greater.
Three key pillars of AI study for students
The Master’s degree course takes a highly technical, hands-on approach to learning. Students are continuously assessed on the 60-credit course and can either study full-time for a year, or part-time, online over two years.
The course revolves around the three main pillars of artificial intelligence applications. The first is model formalisation and data analysis, to represent a business domain and understand its behaviour so far. Secondly, machine learning, which generates data-driven models of the domain, enabling interpretation/interaction of reality as well as foreseeing/predicting future events.
Lastly, machine reasoning applies complex mathematical formalisms to enable the very best decision-making among the myriad possibilities. There are also a number of elective modules, dependent on individuals’ particular interests. For example, now with generative AI, many students opt for Natural Language Processing (NLP).
Coding experience really counts
Murphy emphasises the need for candidates to have already a high level of programming efficiency and preferably a background in computer science. “The primary consideration is people’s coding experience,” he says.
