Module:

Latest Module Specifications

Current Academic Year 2025 - 2026

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Module Title
Module Code	(ITS: CA684A)
Faculty		School
NFQ level		Credit Rating

Description

This module will introduce students to a set of intelligent algorithms applied in modern computing, develop theoretical and mathematical underpinnings of these intelligent algorithms, show how these algorithms can be used in problem solving environments and understand their properties and limitations and gain experience with working with these algorithms.

Learning Outcomes

1. 1DF15A4C-F628-0001-12BD-2E626E8F15ED
2. Collect and clean structured and unstructured data from a variety of sources including structured databases, web services, and text-based data formats;
5. 1
6. 1DF15A4C-F8E3-0001-64F2-DB051A001699
7. Understand key techniques for mining association rules from frequent item sets;
10. 2
11. 1DF15A4C-FCA2-0001-3876-1C25DCB07200
12. Understand and apply a priori algorithm to real datasets;
15. 3
16. 1DF15A4D-03F3-0001-5375-143815691A85
17. Design and implement the workflow required to solve key analytics challenges including recommendation engines, trading algorithms, fault detection, event detection and prediction, etc.;
20. 4
21. 1DF15A4D-0646-0001-29BE-1400160075A0
22. Understand the characteristics and limitations of several different classification and clustering techniques and select the one most appropriate for a given task and dataset;
25. 5
26. 1DF15A4D-0A21-0001-A2D9-330043E9159B
27. Understand and apply key algorithms such as k-Means, SVM, kNN, Naive Bayes, Active Learning, Neural Networks (supervised and unsupervised), scalable approaches including Panda and CART;
30. 6

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	36	No Description
Independent Study	139	Following coursework laid out on Moodle
Total Workload: 175

Type	Description	% of total	Assessment Date
Assessment Breakdown
Group project	n/a	25%	Week 12
Formal Examination	n/a	75%	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