Machine Learning

Certificate

Course Details

Course Code	GA_KCMLG_N09
Level	9
Duration	1 semester
Credits	10
Method of Delivery	Online
Campus Locations	Galway City – Dublin Road
Mode of Delivery	Part Time

Apply via Springboard How to Apply

Apply via Springboard How to Apply

Course Overview

This special purpose award provides a comprehensive grounding in Machine Learning (ML) algorithms and their application in a multi-disciplinary range of domains.

The course will cover the 4 areas of ML:

Supervised Learning (Classification)
Unsupervised Learning (Clustering)
Regression (Predictive modelling)
Dimensionality reduction

The course will also cover practical aspects, including dataset pre-processing and will describe techniques for algorithm selection and parameterisation. Additionally, the process for training models and reporting model performance will be examined.

Course Details

Year 1

Semester	Module Details	Credits	Mandatory / Elective
1	Machine Learning This module provides a comprehensive grounding in Machine Learning (ML) algorithms and their application in a multi-disciplinary range of domains. The course will cover the 4 areas of ML: Supervised Learning (Classification) Unsupervised Learning (Clustering) Regression (Predictive modelling) Dimensionality reduction The module will also cover practical aspects including dataset pre-processing and will describe techniques for algorithms selection and parameterisation. Additionally, the process for training the model and reporting model performance will be detailed. Learning Outcomes 1. Navigate and utilise machine learning algorithms from state-of-the-artlibraries 2. Identify problems that can be modelled using machine learning techniques. 3. Determine the class of problem and the model required, based upon the available data and desired outcome. 4. Pre-process the data for use by machine learning libraries 5. Select an appropriate algorithm and identify a training strategy and parameter set to develop a working model. 6. Analyse and present results on the performance of the model using best-practice techniques.	10	Mandatory

Recommended Study Hours per week

The programme consists of 5 taught hours (lectures, seminars, practical) and 9 hours independent learning.

Examination and Assessment

Continuous assessment and a major programming project will be incorporated into the semester.

Progression

Upon successful completion of this special purpose award students may progress to completing other special purpose awards with a view to building towards the completion of the associated Postgraduate Diploma in Computing of the associated M.Sc. in Computing.

Students may also transfer to other similar cognate programmes of study using this award to gain exemptions therein.

Download a prospectus

Postgraduate Prospectus Online, Flexible & Professional Development Prospectus

Entry Requirements

Successful applications to this programme of study are predicated on the attainment of an Honours Level 8 award in software development or a cognate discipline.

Standard: A Level 8 award in computing (software development) or a closely related discipline with a significant level of software development, e.g. computer engineering or information technology.

Applicants who have an equivalent qualification on the NFQ and industry experience are considered for admission by the programme admissions committee on a case- by-case basis.

English Language Requirements will be as determined by ATU and as published in the Access, Transfer and Progression code. The current requirements are as follows:

Non-EU applicants who are not English speakers are recommended to have a minimum score of 6.0 (with a minimum of 6.0 in each component) in the International English Language Testing System (IELTS) or equivalent.
EU applicants who are not English speakers are recommended to have a minimum score of 6.0 (with a minimum of 6.0 in each component) in the International English Language Testing System (IELTS) or equivalent.

Recognition of Prior Learning
In accordance with the University’s policies ATU is committed to the principles of transparency, equity and fairness in recognition of prior learning (RPL) and to the principle of valuing all learning regardless of the mode or place of its acquisition.

Fees

This course is funded by the Micro-credential Learner Fee Subsidy 2026 programme and fees are subsidised at 80% for all eligible learner categories. Eligible learners can expect to pay €200 for this programme as part of this initiative.

Further information on fees