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Module Specifications

Archived Version 2021 - 2022

Module Title Artificial Intelligence, Info & Info Seeking
Module Code CA652
School School of Computing

Online Module Resources

Module Co-ordinatorProf Alan SmeatonOffice NumberL2 39A
NFQ level 9 Credit Rating 7.5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None
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.

Learning Outcomes

1. Analyse methods by which how human cognition, memory, and work/leisure tasks use computational support
2. Compare a variety of technologies for sensing human activities, including biometrics, and how AI techniques turn this into useful information
3. Understand and compare how search mechanisms, including web search, question answering, information retrieval, and recommender systems operate including how machine learning is used
4. Analyse how multimedia information, covering image and video, can be automatically deconstructed, analysed and understood using AI techniques
5. Understand how linked data and semantic web technologies operate and are used and be able to apply this in implementations.



Workload Full-time hours per semester
Type Hours Description
Online activity40Using MOOCs prepared on the FutureLearn platform, engaging with online discussions and fora, viewing content on FL
Assignment Completion25Completion of assignment based on personal topic choice, validated by lecturer
Directed learning22Discussions and engagement on topics as directed from FutureLearn platform
Independent Study100Independent study on topics from FL, online class discussion and assignments
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

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.

Assessment Breakdown
Continuous Assessment50% Examination Weight50%
Course Work Breakdown
TypeDescription% of totalAssessment Date
Reassessment Requirement
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
Unavailable
Indicative Reading List

    Other Resources

    None
    Programme or List of Programmes
    CAPDPhD
    CAPMMSc
    CAPTPhD-track
    GCCMGraduate Certificate in Computing
    HMSAOStudy Abroad (Humanities & Soc Science)
    MCMM.Sc. in Computing
    NAVNMUNon Award Visitors- UCD/DCU
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