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Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title Artificial Intelligence, Info. & Info Seeking
Module Code CA652D
School School of Computing
Module Co-ordinatorSemester 1: Alessandra Mileo
Semester 2: Alessandra Mileo
Autumn: Alessandra Mileo
Module TeachersAlan Smeaton
Miles Kennedy
Alessandra Mileo
Liting Zhou
NFQ level 9 Credit Rating 10
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None
Repeat the module
No option to resit except by repeating the module.
Description

This module is divided into 5 themes, namely Human Cognition and Information Seeking, Sensing People and Sensing Context, Search Mechanics and AI, Media Analysis and Machine Learning, and Semantic Web/Linked Data. The aim of the module is to familiariase students with a range of information seeking activities we pursue as part of day-to-day life and how AI techniques are used in these information seeking activities. This module looks at data protection and data privacy issues in the context of information access, specifically privacy by design and data anonymisation.

Learning Outcomes

1. Understand how human cognition, memory, and work/leisure tasks use computational support.
2. Understand a variety of technologies for sensing human activities, including biometrics, and how AI techniques turn this into useful information.
3. Understand how search mechanisms, including web search, question answering, information retrieval, and recommender systems operate including how machine learning is used.
4. Understand how multimedia information, covering image and video, can be analysed and understood using AI techniques.
5. Understand how linked data and semantic web technologies operate and are used.
6. Apply privacy by design principles to data access and processing systems.
7. Classify anonymisation and de-identification techniques for working with data.



Workload Full-time hours per semester
Type Hours Description
Independent Study187FutureLearn
Lecture12Privacy by Design and data anonymisation techniques
Independent Study51Privacy assignments, reading and independent study
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

The module is delivered using the FutureLearn platform and is divided into 5 themes, each hosted on a separate MOOC, as
1. human cognition, how the human memory works, how we remember and forget, and how a variety of work/leisure tasks that we perform daily benefit from the use of computational support 2. a variety of technologies for sensing human activities, behaviour, location, including biometrics (HR, RR, GSR, EEG), and how AI techniques and in particular how machine learning turns this into useful information 3. search mechanisms, including basic information retrieval, word stemming, inverted files, and them moving on to web search, PageRank, PANDA, positive and negative weighting factors. The theme also covers question answering, and how recommender systems operate including how machine learning is used to learn profiles and learning-to-rank 4. how multimedia information, covering image and video, can be analysed and understood using AI techniques. This includes convolutional neural networks, ImageNet, TRECvid and how this is now used in systems like FaceBook 5. linked data and semantic web technologies, how they operate and how they are used. 6. Privacy by design and anonymisation techniques (this content is delivered traditionally, not on FutureLearn).

Assessment Breakdown
Continuous Assessment50% Examination Weight50%
Course Work Breakdown
TypeDescription% of totalAssessment Date
ProjectPractical project on information access50%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 1
Indicative Reading List

    Other Resources

    None
    Programme or List of Programmes
    EMLDAIEM in Law, Data &Artificial Intelligence
    MDPPLCMA in Data Protection and Privacy: Law
    Archives:

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