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Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title Artificial Intelligence Methods
Module Code CA639
School School of Computing
Module Co-ordinatorSemester 1: Suzanne Little
Semester 2: Suzanne Little
Autumn: Suzanne Little
Module TeachersSuzanne Little
NFQ level 9 Credit Rating 10
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None
Repeat the module
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



Workload Full-time hours per semester
Type Hours Description
Seminars50Attendance at intensive seminar and workshop sessions
Group work100Group work on addressing challenge or collaborative problem solving at each strand workshop.
Independent Study50Preparation of materials for the seminars
Assignment Completion50Preparation of presentation on topic at each of 5 strand sessions
Total Workload: 250

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.

Assessment Breakdown
Continuous Assessment100% Examination Weight0%
Course Work Breakdown
TypeDescription% of totalAssessment Date
PortfolioEach 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;
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
This module is category 2
Indicative Reading List

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

None
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