Module: Artificial Intelligence Methods

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Artificial Intelligence Methods
Module Code	CSC1130 (ITS: CA639)
Faculty	Computing	School	Engineering & Computing
NFQ level	9	Credit Rating	10

Description

In this module, postgraduate research students, as part of the SFI Centre for Research Training in Artificial Intelligence (AI), will develop their core skills in the theory and application of artificial intelligence. This will involve five strands of study taken from topics such as Personalisation; Optimisation and Constraint Programming; Natural Language Processing; Machine Learning; Visual Media Processing; Ethics of Data Analytics and Fair, Accountable, Transparent AI. Each strand will be delivered by an expert and will consist of one intensive week each (five weeks in total) of blended learning activities - seminars, workshops, practical tasks, group work and independent preparation. Students will develop critical skills in identifying, critiquing and applying suitable AI-based solutions both individually and in groups and learn from external experts about the latest research developments.

Learning Outcomes

1. Explain recent advances in specific AI application areas relevant to their research project such as Personalisation; Optimisation and Constraint Programming; Natural Language Processing; Machine Learning; and/or Visual Media Processing.
2. Identify appropriate AI techniques to address specific challenges
3. Design experiments to establish AI principals
4. Collaborate in a group to build AI based technology solutions
5. Describe the consequences for fair, accountable and transparent systems that use AI
6. Explain ethical aspects applying to AI

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Seminars	50	Attendance at intensive seminar and workshop sessions
Group work	100	Group work on addressing challenge or collaborative problem solving at each strand workshop.
Independent Study	50	Preparation of materials for the seminars
Assignment Completion	50	Preparation of presentation on topic at each of 5 strand sessions
Total Workload: 250

Section Breakdown
CRN	10619	Part of Term	Semester 1 & 2
Coursework	0%	Examination Weight	0%
Grade Scale	PASS/FAIL	Pass Both Elements	Y
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Suzanne Little	Module Teacher

Type	Description	% of total	Assessment Date
Assessment Breakdown
Portfolio	Each strand will be assessed during the intensive week through participation in seminars and deliverables produced, e.g. oral presentations, slideware and short reports. Students will produce Jupyter notebooks (or equivalent documentation) and software that implements and evaluates AI-based solutions both as individual work and within a group.	100%	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

Participation in workshops
Attendance at strand-specific workshops, preparation of and delivery of presentations based on the topics and challenges.

Group work on creative solutions to a challenge task
Collaborate on challenge-driven tasks to understand, identify appropriate technical solutions and apply them. For example, based on similar challenge tasks at conferences (such as ACM RecSys, MediaEval, TREC, etc.). The groups will implement, evaluate and present their solutions to the cohort during intensive workshops.

Indicative Reading List

Books:

Kaplan, Jerry: 2016, Artificial intelligence: what everyone needs to know, 9780190602390
Ramsey, William M.; Frankish, Keith: 2014, The Cambridge handbook of artificial intelligence, 9780521691918

Articles:
None

Other Resources

None