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As such, this is a point in time view of data which will be refreshed periodically.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 No Banner module data is available
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Description This module will introduce students to a set of intelligent algorithms applied in modern computing, develop theoretical and mathematical underpinnings of these intelligent algorithms, show how these algorithms can be used in problem solving environments and understand their properties and limitations and gain experience with working with these algorithms. | |||||||||||||||||||||||||||||||||||||||||||
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Learning Outcomes 1. Collect and clean structured and unstructured data from a variety of sources including structured databases, web services, and text-based data formats; 2. Understand key techniques for mining association rules from frequent item sets; 3. Understand and apply a priori algorithm to real datasets; 4. Design and implement the workflow required to solve key analytics challenges including recommendation engines, trading algorithms, fault detection, event detection and prediction, etc.; 5. Understand the characteristics and limitations of several different classification and clustering techniques and select the one most appropriate for a given task and dataset; 6. Understand and apply key algorithms such as k-Means, SVM, kNN, Naive Bayes, Active Learning, Neural Networks (supervised and unsupervised), scalable approaches including Panda and CART; | |||||||||||||||||||||||||||||||||||||||||||
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
Understand key techniques for mining association rules from frequent item setsUnderstand and apply the a priori algorithm to real datasetsCollect and clean structured and unstructured data from a variety of sources including databases, web services, and text-based data formatsDesign and implement the workflow required to solve key analytics problems such as recommendation engines, trading algorithms, fault detection, event prediction etc.Understand the characteristics and limitations of several different classification and clustering techniques and select the one most appropriate for a given task and the data | |||||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List | |||||||||||||||||||||||||||||||||||||||||||
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Other Resources None | |||||||||||||||||||||||||||||||||||||||||||