OVERVIEW

The widespread adoption of AI in sectors like healthcare, finance, agriculture, and manufacturing has led to a significant skills shortage. Employers are increasingly seeking graduates with strong technical AI knowledge who can identify opportunities for AI applications and effectively implement current AI solutions to address industry challenges.

The Postgraduate Certificate in AI is designed to provide learners with an in-depth technical understanding of core AI concepts and techniques covering machine learning, deep learning and optimisation. Learners are also equipped with the skills to select/apply and evaluate appropriate AI techniques to solve real-world problems from a range of different domains.  

Learners will complete three 5 credit modules in each semester. The first semester will provide an essential grounding in core AI technology. In semester 2 learners will complete advanced specialised AI topics The majority of modules are 100% project-based assessments so that learners obtain hands-on exposure in using AI to solve real-word problems.   

Graduates of the Postgraduate Certificate in Artificial Intelligence will emerge as skilled professionals equipped with the technical expertise and practical experience required to design, develop, and implement AI-driven solutions. They will have a strong foundation in machine learning, deep learning, metaheuristic optimisation and knowledge representation enabling them to tackle real-world AI challenges across a variety of industries.

There is a direct progression route available from the Postgraduate Certificate in Artificial Intelligence to the MSc in Artificial Intelligence. Graduates of the Postgraduate Certificate can apply to complete an additional 30 credits to obtain the MSc award.

Resources for applicants

As this is an expert level programme, it is essential for applicants to have a strong proficiency in mathematics, including statistics, and an advanced level of coding competency in Python and C++. To help you prepare for this programme we offer a number of tutorials which we expect you to complete in full before starting this programme in September.

Programme Aim & Modules

The aim of this programme is to produce expert AI developers. 

Please see the modules offered below and the programme schedule is in the link.

Semester 1

Practical Machine Learning (Mandatory)
Practical Machine Learning will provide a comprehensive foundation in the application, implementation and evaluation of machine learning techniques. The assessments are 100% project based so that learners will step through the machine learning lifecycle, building and optimizing effective machine learning models.  

Metaheuristic Optimisation (Mandatory)
Optimisation has been applied to solve diverse problems that range from route and load optimization in logistics to minimising energy usage in smart grids. Learners will be equipped with the skills to solve complex combinatorial problems through the analysis, design and development of metaheuristic optimization techniques.

Knowledge Representation (Mandatory)
Knowledge representation (KR) is a fundamental area of AI that focuses on the representation of domain specific knowledge. The module will focus on the application of KR to real-world problems such as the semantic web, time-series indexing and temporal abstraction of expert knowledge.
 

Semester 2

Deep Learning (Mandatory)        
Deep learning has had a transformative impact on AI and is now pervasive across a diverse range of domains. This module focuses on the theoretical and practical skills that will enable students to build and apply a range of deep learning models to real-world problems.

Research Practice and Ethics (Mandatory)
An obstacle to the adoption of AI in industry is the identification of suitable use cases. This module equips students with the skills to undertake research to identify projects where AI techniques can be applied effectively.

Natural Language Processing (Elective)
Natural Language Processing has made huge advancements with the advent of transformers and large language models. This elective module will provide learners with an in-depth understanding of how to apply and evaluate state of the art NLP models for use cases such as machine translation

Machine Vision (Elective)
The module will provide a comprehensive overview of the application and implementation of computer vision techniques to real-world problems such as tracking relevant features from image and video data

ADMISSION REQUIREMENTS

Applicants are typically expected to hold an honours degree (Level 8) in Computer Science or a cognate area showing a clear proficiency in a high-level computer programming (Python) and a competency in mathematics (especially probability and statistics). Those with a Level 7 qualification in Computer Science or a cognate area, coupled with at least three years of relevant software development experience in industry will also be considered.

CLOSING DATE FOR APPLICATIONS

Applications for the September 2025 programme is open now. Closing date for applications is 31st August 2025, or whenever the programme is full.

Enquiries

Mr David Murphy
Department of Computer Science
David.Murphy@mtu.ie +353 (21) 4335577

ABOUT THE​ DEPARTMENT OF COMPUTER SCIENCE

The Department of Computer Science at MTU is one of the largest Computer Science departments in Ireland. We offer a range of modern undergraduate programmes and a host of opportunities at master’s degree and at PhD level. Our industry engaged programmes match the needs of our economy and have an excellent reputation for producing the most employable computer science graduates in the region. These highly skilled graduates are in huge demand and contribute significantly to the development of the region. As technology plays a greater role in our society the growth in the demand for these graduates will continue year after year.