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As such, this is a point in time view of data which will be refreshed periodically.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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Coursework Only |
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Description This module will provide students with both a theoretical and practical grounding in modern machine learning. The module includes classical approaches for the purposes of historical context, conceptual development, interpretability and specific solution applicability. However, the primary emphasis is deep learning and contemporary approaches such as reinforcement learning. Both supervised and unsupervised contexts will be explored. Ethical considerations will be given significant emphasis throughout the course. The educational structure is based on a project-oriented approach with a mix of both group and individual activities. Students will gain hands-on experience using research-grade datasets, international challenges and practical tutorial sessions. | |||||||||||||||||||||||||||||||||||||||||||||||||
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Learning Outcomes 1. Implement data pre-processing and feature engineering approaches appropriate to specific problem domains 2. Design a Machine Learning System suitable for a given problem with due consideration given to performance metrics, class/data skew and explainability requirements. 3. Design, train and evaluate a recommender system 4. Design, train and evaluate a machine learning solution for a custom research challenge introduced by instructor based on research being carried out at DCU at PhD level and beyond. 5. Explain machine learning concepts related to algorithm operation, bias/variance, fitting issues, types of ML. 6. Design, train and evaluate a machine learning solution for a sequential data challenge. 7. Describe the ethical issues surrounding artificial intelligence in the context of machine learning | |||||||||||||||||||||||||||||||||||||||||||||||||
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
Feature EngineeringThe importance of features in machine learning including selection, synthesis, extraction. To include feature free approaches. Automatic feature development will be introduced here. Specific approaches for signal processing (time series), images and video and documents will be considered.Similarity Based and Error Based ApproacheskNN, SVM, SVR, Kernel MethodsDeep Neural NetworksBackpropagation, convolutional neural networks, autoencoders, recurrent neural networks, deep neural network architectures, generative adversarial networks.Cross Cutting ContentAutomatic Machine Learning, Ensembling, Ethics, Interpretability, Explainability, Large Scale Machine Learning, Practical Implementation considerations, performance metrics, class and data skew.Ensemble MethodsBagging, boosting, stacking | |||||||||||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List | |||||||||||||||||||||||||||||||||||||||||||||||||
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Other Resources 43214, Book, Sutton & Barto, 2018, Reinforcement learning: An introduction, MIT Press, 43215, Book, Goodfellow, Bengio, Courville, 2016, Deep Learning, | |||||||||||||||||||||||||||||||||||||||||||||||||