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Module Specifications.

Current Academic Year 2024 - 2025

All Module information is indicative, and this portal is an interim interface pending the full upgrade of Coursebuilder and subsequent integration to the new DCU Student Information System (DCU Key).

As such, this is a point in time view of data which will be refreshed periodically. Some fields/data may not yet be available pending the completion of the full Coursebuilder upgrade and integration project. We will post status updates as they become available. Thank you for your patience and understanding.

Date posted: September 2024

Module Title Topics of AI
Module Code CA699I (ITS) / CSC1159 (Banner)
Faculty Engineering & Computing School Computing
Module Co-ordinatorAlessandra Mileo
Module TeachersAlan Smeaton
NFQ level 9 Credit Rating 7.5
Pre-requisite Not Available
Co-requisite Not Available
Compatibles Not Available
Incompatibles Not Available
Repeat examination
Repeat written examination
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



Workload Full-time hours per semester
Type Hours Description
Directed learning36Online learning material on FutureLearn
Independent Study151No Description
Total Workload: 187

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

Assessment Breakdown
Continuous Assessment50% Examination Weight50%
Course Work 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
Reassessment Requirement Type
Resit arrangements are explained by the following categories:
Resit category 1: A resit is available for both* components of the module.
Resit category 2: No resit is available for a 100% continuous assessment module.
Resit category 3: No resit is available for the continuous assessment component where there is a continuous assessment and examination element.
* ‘Both’ is used in the context of the module having a Continuous Assessment/Examination split; where the module is 100% continuous assessment, there will also be a resit of the assessment
This module is category 1
Indicative Reading List

    Other Resources

    39255, Online MOOCs, 0, Course is delivered entireley on the FutureLearn platform,

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