Module: Machine Learning

Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title	Machine Learning
Module Code	CA3109
School	School of Computing
Module Co-ordinator	Semester 1: Claudia Mazo Semester 2: Claudia Mazo Autumn: Claudia Mazo
Module Teachers	Denise Freir Michael Scriney Claudia Mazo
NFQ level	8	Credit Rating	5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

None

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	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

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; 1 = A resit is available for all components of the module 2 = No resit is available for 100% continuous assessment module 3 = No resit is available for the continuous assessment component
This module is category 1

Indicative Reading List

Other Resources

None

Programme or List of Programmes

AI	Chemistry with Artificial Intelligence
CASE	BSc in Computer Applications (Sft.Eng.)
COMSCI	BSc in Computer Science
ECSA	Study Abroad (Engineering & Computing)
ECSAO	Study Abroad (Engineering & Computing)

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