Module: Machine Learning in Context

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Machine Learning in Context
Module Code	CSC1045 (ITS: CA3110)
Faculty	Engineering & Computing	School	Computing
NFQ level	8	Credit Rating	7.5

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.

Learning Outcomes

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2. Explain the key concepts and applications of Machine Learning(ML).
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5. 1
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7. Explain how neural networks can be trained to learn physical concepts.
9. 7
10. 2
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12. Distinguish between supervised and unsupervised Machine Learning(ML) methods (when and how to apply them).
14. 9
15. 3
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17. Apply the appropriate machine learning technique to a given challenge.
19. 8
20. 4
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22. Apply the Cross Industry Standard Process for Data Mining - Crisp-dm framework ML lifecycle Overarching process - iterative process.
24. 8
25. 5
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27. Prepare a case study on a machine-learning solution to a specific research problem in physics.
29. 10
30. 6
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32. Develop a predictive machine-learning model using a physics-relevant data set.
34. 10
35. 7
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37. 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).
39. 11
40. 8

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	18	Module lectures
Debate	12	Discussion
Class Presentation	9	Students will present on topics in ML to other students
Online activity	36	Access to digital resources
Group work	36	Students will work on various aspects of the module in groups
Assignment Completion	36	Students will work on various assignments throughout the module
Independent Study	40.5	Students will study the module material, related resources and references
Total Workload: 187.5

Section Breakdown
CRN	20162	Part of Term	Semester 2
Coursework	100%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	N
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Stephen Power	Module Teacher	Claudia Mazo

Type	Description	% of total	Assessment Date
Assessment Breakdown
Assignment	This assignment will enable students to demonstrate an understanding of machine learning and its application to physical problems	35%	As required
Digital Project	This assessment will involve the development of an artefact which demonstrates an students understanding of method selection and experimental design.	20%	Once per semester
Group presentation	These assessments require students to present (a) on the societal benefits of machine learning with an emphasis on the ethical implications of machine learning and (b) on the application of machine learning to a specific problem in modern physics research.	25%	As required
Oral Examination	This will be an interactive oral exam where students discuss their artifact on method selection and experimental design.	20%	Once per semester

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

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

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.

Indicative Reading List

Books:
None

Articles:
None

Other Resources

None