Module: Machine Learning

Module Specifications.

Current Academic Year 2024 - 2025

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Date posted: September 2024

Module Title	Machine Learning
Module Code	CA4109 (ITS) / CSC1117 (Banner)
Faculty	Engineering & Computing	School	Computing
Module Co-ordinator	Dongyun Nie
Module Teachers	Annalina Caputo, Michael Scriney
NFQ level	8	Credit Rating	7.5
Pre-requisite	Not Available
Co-requisite	Not Available
Compatibles	Not Available
Incompatibles	Not Available

Coursework Only

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.

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

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Lecture	12	Module lectures
Debate	13	No Description
Class Presentation	6	Students will present on topics in ML to other students
Online activity	20	Access to digital resources
Independent Study	50	Students will study the module material, related resources and references
Assignment Completion	48	Students will work on various assignments throughout the module
Group work	36	Students will work on various aspects of the module in groups
Total Workload: 185

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

Assessment Breakdown
Continuous Assessment	100%	Examination Weight	0%

Type	Description	% of total	Assessment Date
Course Work Breakdown
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: Resit category 1: A resit is available for both* components of the module. Resit category 2: No resit is available for a 100% continuous assessment module. Resit category 3: No resit is available for the continuous assessment component where there is a continuous assessment and examination element.
* ‘Both’ is used in the context of the module having a Continuous Assessment/Examination split; where the module is 100% continuous assessment, there will also be a resit of the assessment
This module is category 1

Indicative Reading List

Other Resources

None