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Latest Module Specifications

Current Academic Year 2025 - 2026

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Module Title
Module Code (ITS: CA699I)
Faculty School
NFQ level Credit Rating
Description

This module provides students with a grounding in some of the most important topics in real world applications of Artificial Intelligence. Topics to be covered include human cognition, sensing people and sensing their contexts, computer vision and machine learning, search and information retrieval, semantic web and linked data. The techniques covered in the machine learning component have applicability right across the AI landscape including Fintech, education, language processing, and more.

Learning Outcomes

1. Understand the structure of the brain and how human cognition, human memory, and human thought processes work based as targets for what Artificial Intelligence applications should strive for an aim at
2. Understand how we can use AI techniques to sense and understand human activities, biometrics and behaviour and in particular how machine learning can be used. Uses of machine learning in this context is also applicable to other AI application areas
3. Information seeking and searching and information finding are the most common tasks we do in our day-to-day lives. This module equips the student with knowledge and understanding of how AI applications like information seeking, question answering, information retrieval, searching and in particular web search, operate
4. The first and possibly still most successful application of machine learning is in image and video processing. This module offers the student an understanding and appreciation of how machine learning as an application of AI, has evolved and has allowed computer vision applications like object and activity recognition, to become robust, effective and mainstream
5. An understanding of how linked data and semantic web technologies operate and are used


WorkloadFull time hours per semester
TypeHoursDescription
Directed learning36Online learning material on FutureLearn
Independent Study151No Description
Total Workload: 187
Assessment Breakdown
TypeDescription% of totalAssessment Date
Extended Essay / DissertationAn essay on a topic agreed between lecturer and student, done individually and based on some agreed topic relevant to the course50%Once per semester
Formal ExaminationWritten exam50%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

  • Online MOOCs: Course is delivered entireley on the FutureLearn platform,

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