Module Details

Module Code: COMP8043
Title: Machine Learning
Long Title: Machine Learning
NFQ Level: Advanced
Valid From: Semester 1 - 2018/19 ( September 2018 )
Duration: 1 Semester
Credits: 5
Field of Study: 4811 - Computer Science
Module Delivered in: 2 programme(s)
Module Description: The module will provide a comprehensive foundation in the application and implementation of machine learning techniques. The module will focus on supervised and unsupervised learning algorithms, specifically classification, regression and clustering techniques. It will also look at the theory of optimization and examine its application to high dimensional search spaces.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Apply machine learning methodologies to facilitate pre-processing, dimensionality reduction and model selection.
LO2 Select and apply appropriate machine learning algorithms to datasets from a specific application domain.
LO3 Analyse and evaluate the performance of machine learning algorithms.
LO4 Develop a machine learning algorithm for solving a real-world problem.
LO5 Implement and apply optimization algorithms for solving complex problems with a high dimensional search space.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
12814 SOFT8032 Programming for Data Analytics
13612 COMP8042 Scientific Prog in Python
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 
Indicative Content
Methodology and Evaluation
Application of a standard machine learning methodology using techniques such as dimensionality reduction, model selection, feature selection and hyper-parameter optimization. Overview of evaluation methods such as precision, recall, confusion matrices, learning curves, ROC curves.
Classification Algorithms
Mainstream classification algorithms such as Decision Trees, Ensemble Technique (Bagging and Boosting), Support Vector Machines, Naïve Bayes, Bayesian Networks, Logistical Regression, Instance- Based Learning and Deep Learning.
Regression Algorithms
Introduction to the area of regression. Univariate and multi-variate linear regression, neural networks, ridge regression. How to avoid overfitting through the use of regularization.
Unsupervised Learning Algorithms
Overview of unsupervised learning techniques. Example applications of clustering techniques. Introduction to algorithms such as K-Means, K-Median, DBScan. Optimization and distortion cost function. Random initialization and methods of selecting number of clusters.
Optimization
Introduction to the area of optimization. Categories of optimization such as meta-heuristic and constraint-based optimization. Informed/Uninformed search strategies. Meta-heuristic optimization algorithms. Introduce the concept of heuristic algorithms such as hill climbing, simulated annealing, evolutionary, particle swarm optimization (PSO) and ant colony optimization (ACO).
Case Study
Design and implementation of a relevant case study such as a recommender system.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Project % of Total Mark 30
Timing Week 6 Learning Outcomes 1,2,3
Assessment Description
Perform a comparative analysis of the iterative application of a range of machine learning algorithms to a dataset from an application domain. Standard methodologies should be applied and the performance should be comprehensively evaluated. Findings should be documented.
Assessment Type Project % of Total Mark 35
Timing Week 9 Learning Outcomes 3,4
Assessment Description
Design and develop a machine learning application for a case study project and evaluate its performance.
Assessment Type Project % of Total Mark 35
Timing Sem End Learning Outcomes 5
Assessment Description
Implement an optimization algorithm for solving a complex problem with a high dimensional search space.
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

The University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Delivers the concepts and theories underpinning the learning outcomes. Every Week 2.00 2
Lab Contact Application of learning to case studies and project work. Every Week 2.00 2
Independent Learning Non Contact Student reads recommended papers and practices implementation. Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Delivers the concepts and theories underpinning the learning outcomes. Every Week 2.00 2
Lab Contact Application of learning to case studies and project work. Every Week 2.00 2
Independent Learning Non Contact Student reads recommended papers and practices implementation. Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
 
Module Resources
Recommended Book Resources
  • Sebastian Raschka. (2015), Python Machine Learning, Packt Publishing, [ISBN: 9781783555130].
  • John Hearty. (2016), Advanced Machine Learning with Python, 1st. Packt Publishing, [ISBN: 9781784398637].
Supplementary Book Resources
  • Peter Flach. (2012), Machine Learning: The Art and Science of Algorithms that Make Sense of Data, Cambridge University Press, [ISBN: 9781107422223].
  • Ethem Alpaydin. (2016), Machine Learning: The New AI, MIT Press, [ISBN: 9780262529518].
  • Tom M. Mitchell. (1997), Machine learning, McGraw-Hill, New York, [ISBN: 9780070428072].
Recommended Article/Paper Resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_KSDEV_8 Bachelor of Science (Honours) in Software Development 7 Mandatory
CR_SDAAN_8 Higher Diploma in Science in Data Science & Analytics 2 Elective