FAO Applicants Resident in Ireland: Springboard+ Funding
This course has been funded under the Irish Government’s ‘Springboard+’ programme, for more information on the fee structure please head to the Springboard+ website here. To be eligible you need to be:
- Resident in Ireland - either with a valid social welfare claim in place for unemployed people; or, for employed people, proof of having been resident here for 3 out of last 5 years.
- Non-EU Irish residents must have ‘Stamp 4’ residency immigration status or above in order to be eligible.
- Recent Graduates of full-time level 8 degree programmes are not eligible for Springboard+ funding for one year after graduation date but may apply as fee-paying students.
- The funding is not open to people who are not resident in Ireland. Please see further information on Springboard+ eligibility.
- The Springboard+ application closing date is Thursday, 12 June 2026.
About Programme
The Certificate in Artificial Intelligence & Machine Learning Engineering is a part-time, online programme designed for learners who want to build practical, job-ready skills in applied AI and machine learning. It focuses on the full engineering workflow—data preparation, model development, evaluation, and deployment—so you can take an AI idea from concept to working solution.
This programme is ideal for Computing graduates (or those with equivalent experience) seeking to upskill or reskill, Professionals who want to move into AI/ML engineering, data roles, or AI-enabled software development, or graduates from the Certificate in Computer Science who want to specialise in modern AI.
The programme team has extensive experience in fostering a vibrant learning community, helping you feel part of a real class. This is achieved using industry-standard collaboration tools alongside our custom-built e-learning platform, developed by staff and students. The programme also includes optional onsite workshops, podcasts, interviews, and a wide range of study supports.
The programme also includes a a substantial applied project where learners deliver a working AI solution, not just a report.
Delivery
This programme is part-time and fully online. Classes are live-streamed and recorded, allowing students to engage at times that suit their individual schedules and commitments.
Inspired by real-world software development practices, we’ve adopted an "agile semester" approach to content delivery. The semester is broken down into four 3-week "sprints," separated by a reading week. This structure has proven effective in similar part-time programmes, helping students to better consume and manage the course content.
Data Handling and Infrastructure for AI (10 ECTS):
Introduces the data handling and infrastructure foundations needed for AI/ML, including working with structured and unstructured data, feature engineering, and building data pipelines (batch and streaming). It also covers scalable infrastructure, cloud platforms, and AI/ML Ops practices for training and running modern AI systems.
The Evolution of Machine Learning and AI (5 ECTS):
Explores how AI and machine learning have evolved—from early symbolic systems and classical ML through deep learning to today’s transformers, diffusion models, and generative AI. It also examines the impact of large language models on software development and introduces ethical and responsible AI themes.
Building Machine Learning Models for AI Systems (5 ECTS):
Focuses on building reliable and explainable ML models, using key metrics and evaluation methods to improve performance. It covers issues like overfitting, bias detection, adversarial training, and robustness testing (including drift) to support fair, trustworthy AI systems.
Applied AI Project (10 ECTS):
A problem-based capstone where learners deliver an end-to-end AI solution: define the problem, collect and preprocess data, select/train/fine-tune models, validate results, and deploy using modern approaches such as APIs and containerisation. Projects may include NLP, computer vision, or recommender systems.
Level 7 graduates with additional experiential and/or certified learning may be considered for entry to the programme under the SETU Recognition of Prior Learning (RPL) process.
HDip in Computer Science
MSc Enterprise Software Systems
This programme ensures graduates develop industry-relevant skills. Industry-standard practices are integrated into all modules, ensuring students develop essential workplace skills such as online communication & collaboration (Slack), agile methodologies (project management), Version control (GitHub). Assessments reward engagement with these practices, reinforcing their real-world importance and ensuring students gain practical, industry-ready experience.
Course Leader
