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COMP8043 - Machine Learning

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Title:Machine Learning
Long Title:Machine Learning
Module Code:COMP8043
 
Credits: 5
NFQ Level:Advanced
Field of Study: Computer Science
Valid From: Semester 1 - 2017/18 ( September 2017 )
Module Delivered in 2 programme(s)
Module Coordinator: TIM HORGAN
Module Author: Ted Scully
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:
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.
Pre-requisite learning
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 CIT module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
9098 COMP8042 Analytical and Scientific Prog
12814 SOFT8032 Programming for Data Analytics
12825 COMP8043 Machine Learning
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
Co-requisites
No Co Requisites listed
 

Module Content & Assessment

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.
Assessment Breakdown%
Course Work100.00%
Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Project 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. 1,2,3 30.0 Week 6
Project Design and develop a machine learning application for a case study project and evaluate its performance. 3,4 35.0 Week 9
Project Implement an optimization algorithm for solving a complex problem with a high dimensional search space. 5 35.0 Week 12
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 institute reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Delivers the concepts and theories underpinning the learning outcomes. 2.0 Every Week 2.00
Lab Application of learning to case studies and project work. 2.0 Every Week 2.00
Independent Learning Student reads recommended papers and practices implementation. 3.0 Every Week 3.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Delivers the concepts and theories underpinning the learning outcomes. 2.0 Every Week 2.00
Lab Application of learning to case studies and project work. 2.0 Every Week 2.00
Independent Learning Student reads recommended papers and practices implementation. 3.0 Every Week 3.00
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 Ed., 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

Cork Institute of Technology
Rossa Avenue, Bishopstown, Cork

Tel: 021-4326100     Fax: 021-4545343
Email: help@cit.edu.ie