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As such, this is a point in time view of data which will be refreshed periodically.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
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Repeat the module No option to resit except by repeating the module. |
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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. | |||||||||||||||||||||||||||||||||||||||||||
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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. | |||||||||||||||||||||||||||||||||||||||||||
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 |
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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, as1. 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). | |||||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List | |||||||||||||||||||||||||||||||||||||||||||
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Other Resources None | |||||||||||||||||||||||||||||||||||||||||||