Module: Introduction to Machine Learning & Data Analytics

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Introduction to Machine Learning & Data Analytics
Module Code	CSC1169 (ITS: CA4045)
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. Explain 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. Apply 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. 1E495EFD-2825-0001-C2B9-4EC8125EDE40
22. Investigate the use of training/test data sets
23.
24. 7,12,6,8,9
25. 5
26. 1E495EFD-365A-0001-F41E-1D801CF718FF
27. Apply the Cross Industry Standard Process for Data Mining - Crisp-dm framework ML lifecycle Overarching process - iterative process
28.
29. 20,7,12,15,6,13,19,14
30. 6
31. 1E495EFD-4A70-0001-CC25-1E20E0D0119A
32. Analyse the potential impact of machine learning in the context of sustainability
33.
34. 22,20,7,12,6,13,19,21,9
35. 7
36. 1E495EFD-5C94-0001-56B6-18301B2C9630
37. Analyse the difference between ML research and real-world analysis needs
38.
39. 17,20,7,12,6,13,19
40. 8

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	24	Module lectures
Independent Study	101	Students will study the module material, related resources and references
Total Workload: 125

Section Breakdown
CRN	11939	Part of Term	Semester 1
Coursework	50%	Examination Weight	50%
Grade Scale	40PASS	Pass Both Elements	N
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Andrew Mccarren	Module Teacher	Denise Freir

Type	Description	% of total	Assessment Date
Assessment Breakdown
Assignment	This assignment will enable students to demonstrate an understanding of machine learning	10%	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.	10%	n/a
Digital Project	This assessment will involve the development of an artefact which demonstrates an students understanding of method selection and experimental design.	15%	n/a
Oral Examination	This will be an interactive oral exam where students discuss their artefact on method selection and experimental design.	15%	n/a
Formal Examination	n/a	50%	End-of-Semester

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