Latest Module Specifications
Current Academic Year 2025 - 2026
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Description The purpose of this module is to provide an overview of the tools, techniques and purpose of machine learning. Students will participate in a variety of innovative teaching activities to distinguish approaches and supervised and unsupervised methodologies within machine learning. In addition, they will evaluate the effects and implications of Machine Learning(ML) on sustainability and society and the role of explainable-AI on ML adoption. The module is underpinned by Challenge Based Learning (CBL) which is an emerging pedagogy that involves students working in teams with stakeholders to investigate and co-create solutions for significant societal problems will follow a three phase approach (Engage - Investigate - Act). Through a process of collaborative engagement with peers, academics, and stakeholders, students will develop solutions to real-world challenges. Students will also investigate the application of machine-learning techniques to specific problems in the discipline of Physics, such as exoplanet detection, materials discovery or feature detection in microscopy. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Learning Outcomes 1. Explain the key concepts and applications of Machine Learning(ML). 2. Explain how neural networks can be trained to learn physical concepts. 3. Distinguish between supervised and unsupervised Machine Learning(ML) methods (when and how to apply them). 4. Apply the appropriate machine learning technique to a given challenge. 5. Apply the Cross Industry Standard Process for Data Mining - Crisp-dm framework ML lifecycle Overarching process - iterative process. 6. Prepare a case study on a machine-learning solution to a specific research problem in physics. 7. Develop a predictive machine-learning model using a physics-relevant data set. 8. Evaluate a given machine learning solution from different perspectives (performance metrics, human evaluation, ethics ) in a general and domain specific context (physics-relevant data set). | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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
Applications of Machine Learning in Physics Case studies highlighting the application of Machine Learning(ML) techniques to make predictions in different areas of physics, including astrophysics, materials, microscopy and statistical mechanics. Hands-on Prediction of Physical Properties How to prepare physical data sets and choose appropriate ML methods to make predictions. Learning Physical Concepts How neural networks represent data and how autoencoder networks can “learn” physical models. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Indicative Reading List Books: None Articles: None | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Other Resources None | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||