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COMP9061 - Practical Machine Learning

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Title:Practical Machine Learning
Long Title:Practical Machine Learning
Module Code:COMP9061
 
Credits: 5
NFQ Level:Expert
Field of Study: Computer Science
Valid From: Semester 1 - 2018/19 ( September 2018 )
Module Delivered in 1 programme(s)
Module Coordinator: TIM HORGAN
Module Author: Ted Scully
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:
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.
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).
No recommendations listed
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
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.
Assessment Breakdown%
Course Work100.00%
Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Project Develop a machine learning model for a real-world problem and perform a comprehensive analysis. 1 50.0 Week 9
Project 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. 2,3,4 50.0 Week 13
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 undertakes independent study. The 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 undertakes independent study. The 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, 2nd Ed., Packt [ISBN: 9781783555130]
  • John Hearty 2016, Advanced Machine Learning with Python, 1st Ed., Packt Publishing [ISBN: 9781784398637]
Supplementary Book Resources
  • Ethem Alpaydin 2016, Machine Learning: The New AI, 1st Ed. [ISBN: 9780262529518]
  • Peter Flach 2012, Machine Learning: The Art and Science of Algorithms that Make Sense of Data, 1st Ed., 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

Cork Institute of Technology
Rossa Avenue, Bishopstown, Cork

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