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Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title Machine Learning
Module Code CSC1145 (ITS: CA684A)
Faculty Computing School Engineering & Computing
NFQ level 9 Credit Rating 7.5
Description

This module will introduce students to a set of intelligent algorithms applied in modern computing, develop theoretical and mathematical underpinnings of these intelligent algorithms, show how these algorithms can be used in problem solving environments and understand their properties and limitations and gain experience with working with these algorithms.

Learning Outcomes

1. Collect and clean structured and unstructured data from a variety of sources including structured databases, web services, and text-based data formats;
2. Understand key techniques for mining association rules from frequent item sets;
3. Understand and apply a priori algorithm to real datasets;
4. Design and implement the workflow required to solve key analytics challenges including recommendation engines, trading algorithms, fault detection, event detection and prediction, etc.;
5. Understand the characteristics and limitations of several different classification and clustering techniques and select the one most appropriate for a given task and dataset;
6. Understand and apply key algorithms such as k-Means, SVM, kNN, Naive Bayes, Active Learning, Neural Networks (supervised and unsupervised), scalable approaches including Panda and CART;


WorkloadFull time hours per semester
TypeHoursDescription
Lecture36No Description
Independent Study139Following coursework laid out on Moodle
Total Workload: 175
Section Breakdown
CRN20416Part of TermSemester 2
Coursework0%Examination Weight0%
Grade Scale40PASSPass Both ElementsY
Resit CategoryRC3Best MarkN
Module Co-ordinatorTomas WardModule Teacher
Section Breakdown
CRN21149Part of TermSemester 2
Coursework0%Examination Weight0%
Grade Scale40PASSPass Both ElementsY
Resit CategoryRC3Best MarkN
Module Co-ordinatorTomas WardModule Teacher
Assessment Breakdown
TypeDescription% of totalAssessment Date
Group project n/a25%Week 12
Formal Examinationn/a75%End-of-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

Indicative Reading List

Books:
None

Articles:
None
Other Resources

None

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