Advanced Machine Learning

Machine Learning has become a defining force in modern technology, shaping breakthroughs in healthcare, finance, robotics, media, and scientific discovery. Its ability to learn patterns from data, adapt to new information, and support automated decision‑making makes it central to solving complex, large‑scale problems across industry and research. As organisations increasingly rely on intelligent systems, the ability to design, train, and critically evaluate machine learning models has become an essential skill for computing professionals.

This module provides learners with a rigorous theoretical foundation and extensive practical experience in contemporary machine learning. While classical algorithms are introduced for conceptual grounding and interpretability, the primary emphasis is on deep learning and modern approaches, including reinforcement learning and advanced neural architectures. Students will explore supervised and unsupervised learning, feature engineering, similarity‑based and error‑based methods, ensemble techniques, and the challenges of large‑scale model development.

A strong ethical dimension runs throughout the module, encouraging learners to critically examine issues of fairness, transparency, explainability, and societal impact in AI systems. The module is delivered through a project‑oriented structure, combining lectures, hands‑on laboratory sessions, and engagement with research‑grade datasets and international challenge problems. Learners will work both individually and collaboratively to design, train, and evaluate machine learning solutions, including recommender systems, sequential models, and custom research challenges aligned with current DCU research activity.

On successful completion of this module, learners will be able to:

• Implement data pre-processing and feature engineering approaches appropriate to specific problem domains.
• Design a Machine Learning System suitable for a given problem with due consideration given to performance metrics, class/data skew and explainability requirements. 
• Describe the fundamental concepts of Data Literacy and Analytics, the key steps in the analytics process, and the applications and implications of data analytics in one’s specialism.
• Design, train and evaluate a recommender system.
• Design, train and evaluate a machine learning solution for a custom research challenge introduced by instructor based on research being carried out at DCU at PhD level and beyond.
• Explain machine learning concepts related to algorithm operation, bias/variance, fitting issues, types of ML.
• Design, train and evaluate a machine learning solution for a sequential data challenge.
• Describe the ethical issues surrounding artificial intelligence in the context of machine learning (Act responsibly when using digital technologies, with due regard for oneself and others.)

A Primary Honours degree, Level 8 in Electronic/Electrical/Computer Engineering, Applied Physics, Computer Sciences or other Cognate/Engineering Disciplines. Applications are also invited from diverse educational and/or employment backgrounds, with applications evaluated on a case-by-case basis.  

And also to indicate the required documentation:

• Please provide Academic Transcripts for final year of study where appropriate (English translation)
• All applicants must submit a copy of their passport

There is no availability for a deferred entry onto a micro-credential.

Good understanding of mathematical concepts in Linear Algebra, Probability, and Calculus would be recommended, as well as strong programming skills, preferably in Python. If applicable, evidence of competence in the English language as per DCU entry requirements. Please see here.

For further information regarding the HCI learner subsidy eligibility criteria please click here.