| Module Title |
Machine Learning |
| Module Code |
CSC1044 (ITS: CA3109) |
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Faculty |
Computing |
School |
Engineering & Computing |
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NFQ level |
8 |
Credit Rating |
5 |
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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 on sustainability and society and the role of explainable-AI on ML adoption.
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Learning Outcomes
1. Understand the purpose and key applications of Machine Learning.
2. Distinguish between supervised and unsupervised Machine Learning methods and when and how to apply them.
3. Understand the concepts and application of machine learning and artificial intelligence in online learning and large data set
applications.
4. Apply and synthesise the characteristics of different methods of machine learning
5. Evaluate the ethics of Big Data
6. Investigate the use of training/test data sets
7. Apply the Cross Industry Standard Process for Data Mining - Crisp-dm framework ML lifecycle Overarching process - iterative
process
8. Analyse the potential impact of machine learning in the context of sustainability
9. Analyse the difference between ML research and real-world analysis needs
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| Workload | Full time hours per semester | | Type | Hours | Description |
|---|
| Lecture | 12 | Module lectures | | Debate | 12 | Discussion | | Class Presentation | 6 | Students will present on topics in ML to other students | | Online activity | 20 | Access to digital resources | | Independent Study | 25 | Students will study the module material, related resources and references | | Assignment Completion | 25 | Students will work on various assignments throughout the module | | Group work | 25 | Students will work on various aspects of the module in groups |
| Total Workload: 125 |
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| Section Breakdown | | CRN | 20161 | Part of Term | Semester 2 | | Coursework | 0% | Examination Weight | 0% | | Grade Scale | 40PASS | Pass Both Elements | Y | | Resit Category | RC1 | Best Mark | N | | Module Co-ordinator | Claudia Mazo | Module Teacher | Denise Freir, Michael Scriney |
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| Assessment Breakdown |
| Type | Description | % of total | Assessment Date |
|---|
| Assignment | This assignment will enable students to demonstrate an understanding of
machine learning | 20% | n/a | | Group presentation | This assessment requires students to present on the societal benefits of
machine learning with an emphasis on the ethical implications of
machine learning and potential impact of machine learning in the context
of sustainability. | 20% | n/a | | Digital Project | This assessment will involve the development of an artefact which demonstrates an students understanding of method selection and
experimental design. | 30% | n/a | | Oral Examination | This will be an interactive oral exam where students discuss their artefact
on method selection and experimental design. | 30% | n/a |
| 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
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Pre-requisite |
None
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Co-requisite |
None |
| Compatibles |
None |
| Incompatibles |
None |
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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
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Indicative Reading List
Books:
None
Articles:
None |
Other Resources
None |
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