Module Details
| Module Code: |
COMP9061 |
| Title: |
Practical Machine Learning
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|
Long Title:
|
Practical Machine Learning
|
| NFQ Level: |
Expert |
| Valid From: |
Semester 2 - 2018/19 ( January 2019 ) |
| Field of Study: |
4811 - Computer Science
|
| 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).
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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.
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| 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 |
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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.
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Evaluation
Best practice evaluation techniques such as precision, recall, confusion matrices and ROC curves. Debugging algorithms using validation and learning curves. Cross fold validation.
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Classification Algorithms
Classification algorithms such as decision trees, ensemble technique (bagging and boosting), support vector machines, instance-based algorithms, naïve bayes, bayesian networks, etc.
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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.
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Case Study
Design and implementation of a relevant case study such as a recommender system.
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Module Content & Assessment
|
| Assessment Breakdown | % |
|---|
| Coursework | 100.00% |
Assessments
| 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
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| Recommended Book Resources |
|---|
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Sebastian Raschka. (2015), Python Machine Learning, 2nd. Packt, [ISBN: 9781783555130].
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John Hearty. (2016), Advanced Machine Learning with Python, 1st. Packt Publishing, [ISBN: 9781784398637].
| | Supplementary Book Resources |
|---|
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Ethem Alpaydin. (2016), Machine Learning: The New AI, 1st. [ISBN: 9780262529518].
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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 |
|---|
-
Pedro Domingos. (2012), A Few Useful Things to Know about
Machine Learning, Communications of the ACM, 55,
| | Other Resources |
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Website, Kaggle: Data Science,
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Website, Scikit-Learn,
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