Module Details

Module Code: COMP9061
Title: Practical Machine Learning
Long Title: Practical Machine Learning
NFQ Level: Expert
Valid From: Semester 2 - 2018/19 ( January 2019 )
Duration: 1 Semester
Credits: 5
Field of Study: 4811 - Computer Science
Module Delivered in: 3 programme(s)
Module Description: Machine learning provides a means by which programs can infer new knowledge from observational data. This module will provide a comprehensive foundation in the theory, application and implementation of machine learning techniques. The module focuses on supervised and unsupervised learning algorithms, specifically classification and clustering techniques.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Develop a machine learning algorithm for solving a real-world problem.
LO2 Perform pre-processing and model selection for machine learning models.
LO3 Select and apply appropriate classification algorithms to datasets from a specific application domain.
LO4 Evaluate the accuracy of machine learning models using best practice techniques.
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).

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
Pre-processing and Model Selection
Application of pre-processing techniques such as outlier detection, feature selection, imputation of missing data, encoding, normalization, etc. Model selection using hyper parameter optimization.
Evaluation
Best practice evaluation techniques such as precision, recall, confusion matrices and ROC curves. Debugging algorithms using validation and learning curves. Cross fold validation.
Classification Algorithms
Classification algorithms such as decision trees, ensemble technique (bagging and boosting), support vector machines, instance-based algorithms, naïve bayes, bayesian networks, etc.
Unsupervised Algorithms
Overview of unsupervised learning techniques. Example applications of clustering techniques. Introduction to algorithms such as k-means, k-median, dbscan and hierarchical clustering techniques. Optimization and distortion cost function. Random initialization and methods of selecting number of clusters. Silhouette plots.
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 50
Timing Week 9 Learning Outcomes 1
Assessment Description
Develop a machine learning model for a real-world problem and perform a comprehensive analysis.
Assessment Type Project % of Total Mark 50
Timing Week 13 Learning Outcomes 2,3,4
Assessment Description
Perform a comparative analysis of a range of machine learning classification algorithms applied to a dataset from an application domain. Standard pre-processing and model selection techniques should be applied and the performance should be comprehensively evaluated. Findings should be documented.
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 undertakes independent study. The 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 undertakes independent study. The 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, 2nd. Packt, [ISBN: 9781783555130].
  • John Hearty. (2016), Advanced Machine Learning with Python, 1st. Packt Publishing, [ISBN: 9781784398637].
Supplementary Book Resources
  • Ethem Alpaydin. (2016), Machine Learning: The New AI, 1st. [ISBN: 9780262529518].
  • Peter Flach. (2012), Machine Learning: The Art and Science of Algorithms that Make Sense of Data, 1st. Cambridge University Press, [ISBN: 9781107422223].
Recommended Article/Paper Resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_KARIN_9 Master of Science in Artificial Intelligence 1 Mandatory
CR_KCLDC_9 Master of Science in Cloud Computing 1 Elective
CR_KCLDC_9 Master of Science in Cloud Computing 2 Elective