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Module Specifications

Archived Version 2022 - 2023

Module Title
Module Code

Online Module Resources

NFQ level 8 Credit Rating 7.5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None

The purpose of this module is to provide an overview of the tools, techniques and purpose of machine learning. Students will participate in a variety of innovative teaching activities to distinguish approaches and supervised and unsupervised methodologies within machine learning. In addition, they will evaluate the effects and implications of machine learning on sustainability and society and the role of explainable-AI on ML adoption.

Learning Outcomes

1. Understand the purpose and key applications of Machine Learning.
2. Distinguish between supervised and unsupervised Machine Learning methods and when and how to apply them.
3. Understand the concepts and application of machine learning and artificial intelligence in online learning and large data set applications.
4. Apply and synthesise the characteristics of different methods of machine learning
5. Evaluate the ethics of Big Data
6. Investigate the use of training/test data sets ….
7. Apply the Cross Industry Standard Process for Data Mining - Crisp-dm framework ML lifecycle Overarching process - iterative process
8. Analyse the potential impact of machine learning in the context of sustainability
9. Analyse the difference between ML research and real-world analysis needs

Workload Full-time hours per semester
Type Hours Description
Lecture12Module lectures
Debate13No Description
Class Presentation6Students will present on topics in ML to other students
Online activity20Access to digital resources
Independent Study50Students will study the module material, related resources and references
Assignment Completion48Students will work on various assignments throughout the module
Group work36Students will work on various aspects of the module in groups
Total Workload: 185

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Assessment Breakdown
Continuous Assessment% Examination Weight%
Course Work Breakdown
TypeDescription% of totalAssessment Date
Reassessment Requirement
Resit arrangements are explained by the following categories;
1 = A resit is available for all components of the module
2 = No resit is available for 100% continuous assessment module
3 = No resit is available for the continuous assessment component
Indicative Reading List

    Other Resources

    Programme or List of Programmes