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Parent Programme
Bachelor in Computing (Level 7 NFQ)
NFQ Level 7 & Reference
Level 7 / Ref: M3.11
12 Weeks X 3 Hours per week
Machine Learning (Elective)
Module Credit Units

Machine Learning (Elective)

Introduction to Machine Learning

The aims of this module are to provide learners with an understanding of key concepts, techniques, and practical skills in the field of machine learning. With this grounding learners will be trained to push further and communicate results to a variety of audiences (technical, adept, expert). A focus on the interpretability, repeatability and reproducibility of work to instil trust in results presented will be at the forefront. Key issues in ethics will also be covered as these societal challenges provide varying levels of social acceptability in application areas.

Indicative Syllabus Content

Theoretical base for machine learning

  • Probability and statistics
  • Linear mathematics
  • Supervised learning vs unsupervised learning

Considerations for data in machine learning

  • Data types and structures
  • Pre-processing and cleaning
  • Feature extraction and engineering
  • Data augmentation
  • Data imputation

Unsupervised learning

  • K-means clustering
  • Agglomerative clustering
  • Density-Based Spatial Clustering of Applications with Noise
  • Dimensionality reduction (PCA and UMAPS)

Model fitting

  • Cross validation
  • Over fitting and under fitting
  • Precision and accuracy
  • F1 score

Supervised learning

  • Linear regression
  • Logistic regression
  • Decision trees and random forests
  • K-nearest neighbours
  • Support vector machines


  • Case studies and report writing
  • Transparency and interpretability of results
  • Practical limitations due to ethical concerns

Minimum Intended Learning Outcomes (MIMLOs)

Upon successful completion of this module, the learner should be able to:
Discuss the theory behind machine learning applications and be capable of articulating constructive arguments for or against any particular technique for a given dataset or use case.
Build a machine learning model using common industry tools.
Make arguments behind data augmentation or data imputation as needed for unideal datasets.
Demonstrate consciousness of ethical issues and cite case studies for consequences in a cost/benefit analysis.
Evaluate learning methods and compare using standard metrics and select a model as appropriate for a given task


1, 2, 3, 4, 5
CA 1 - CA 6: Coding Assignments
Total 100%
Proctored Written Exam
All Assessments

Reassessment Opportunity

Where the combined marks of the assessment and examination do not reach the pass mark the learner will be required to repeat the element of assessment that they failed. Reassessment materials will be published on Moodle after the Examination Board and will be aligned to the MIMLOs and learners will be capped at 40% unless there are personal mitigating circumstances.

Aims & Objectives

The Machine Learning module will ensure learners meet the following objectives:

  • Basic knowledge of machine learning concepts
  • Familiarity with unsupervised learning methods
  • Familiarity with supervised learning methods
  • Practical skill with industry tools (python scikit learn)
  • Grounding in ethics specific to machine learning issues
  • Communication skills to deliver reports to a range of audiences