Module: Machine Learning

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Machine Learning
Module Code	CSC1044 (ITS: CA3109)
Faculty	Engineering & Computing	School	Computing
NFQ level	8	Credit Rating	5

Description

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. 1E495EF9-68E6-0001-3F5D-D6E292301A1C
2. Understand the purpose and key applications of Machine Learning.
3.
4. 7,12,6,9,10
5. 1
6. 1E495EF9-7A94-0001-841A-1BFD13D01D57
7. Distinguish between supervised and unsupervised Machine Learning methods and when and how to apply them.
8.
9. 7,12,6,11,9,10
10. 2
11. 1E495EF9-8DB1-0001-EAA0-1BF7E5C01CB0
12. Understand the concepts and application of machine learning and artificial intelligence in online learning and large data set applications.
13.
14. 7,12,6,13,19,11,9,10
15. 3
16. 1E495EF9-A55A-0001-92AD-11701B231127
17. Apply and synthesise the characteristics of different methods of machine learning
18.
19. 17,20,7,12,16,15,6,13,8,19,14,9,10
20. 4
21. 1E495EFA-113A-0001-F1F4-F4225780175E
22. Evaluate the ethics of Big Data
23.
24. 22,20,7,12,6,13,19,21,11,9,10
25. 5
26. 1E495EFD-2825-0001-C2B9-4EC8125EDE40
27. Investigate the use of training/test data sets
28.
29. 7,12,6,8,9
30. 6
31. 1E495EFD-365A-0001-F41E-1D801CF718FF
32. Apply the Cross Industry Standard Process for Data Mining - Crisp-dm framework ML lifecycle Overarching process - iterative process
33.
34. 20,7,12,15,6,13,19,14
35. 7
36. 1E495EFD-4A70-0001-CC25-1E20E0D0119A
37. Analyse the potential impact of machine learning in the context of sustainability
38.
39. 22,20,7,12,6,13,19,21,9
40. 8
41. 1E495EFD-5C94-0001-56B6-18301B2C9630
42. Analyse the difference between ML research and real-world analysis needs
43.
44. 17,20,7,12,6,13,19
45. 9

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	12	Module lectures
Debate	12	Discussion
Class Presentation	6	Students will present on topics in ML to other students
Online activity	20	Access to digital resources
Independent Study	25	Students will study the module material, related resources and references
Assignment Completion	25	Students will work on various assignments throughout the module
Group work	25	Students will work on various aspects of the module in groups
Total Workload: 125

Section Breakdown
CRN	20161	Part of Term	Semester 2
Coursework	100%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	N
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Luca Rossetto	Module Teacher	Claudia Mazo, Denise Freir, Michael Scriney

Type	Description	% of total	Assessment Date
Assessment Breakdown
Assignment	This assignment will enable students to demonstrate an understanding of machine learning	20%	n/a
Group presentation	This assessment requires students to present on the societal benefits of machine learning with an emphasis on the ethical implications of machine learning and potential impact of machine learning in the context of sustainability.	20%	n/a
Digital Project	This assessment will involve the development of an artefact which demonstrates an students understanding of method selection and experimental design.	30%	n/a
Oral Examination	This will be an interactive oral exam where students discuss their artefact on method selection and experimental design.	30%	n/a

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

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

Indicative Reading List

Books:
None

Articles:
None

Other Resources

None