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Due to the exceptional circumstances caused by the COVID-19 pandemic, the learning activities, assessments and attendance requirements for this unit may be subject to late changes. Please refer to this unit outline regularly for up to date information and to notices in the unit’s Canvas site for any adjustments.
Unit of study_
COMP5318: Machine Learning and Data Mining
Machine learning is the process of automatically building mathematical models that explain and generalise datasets. It integrates elements of statistics and algorithm development into the same discipline. Data mining is a discipline within knowledge discovery that seeks to facilitate the exploration and analysis of large quantities for data, by automatic and semiautomatic means. This subject provides a practical and technical introduction to machine learning and data mining. Topics to be covered include problems of discovering patterns in the data, classification, regression, feature extraction and data visualisation. Also covered are analysis, comparison and usage of various types of machine learning techniques and statistical techniques.
| Code | COMP5318 |
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| Academic unit | Computer Science |
| Credit points | 6 |
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Prerequisites:
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None |
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Corequisites:
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None |
| Prohibitions:
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None |
| Assumed knowledge:
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Programming skills |
At the completion of this unit, you should be able to:
- LO1. understand the basic principles, strengths, weaknesses and applicability of machine learning algorithms for solving classification, regression, clustering and reinforcement learning tasks
- LO2. have obtained practical experience in designing, implementing and evaluating machine learning algorithms
- LO3. have gained practical experience in using machine learning software and libraries
- LO4. present and interpret data and information in verbal and written form
Unit outlines
Unit outlines will be available 2 weeks before the first day of teaching for 1000-level and 5000-level units, or one week before the first day of teaching for all other units.
There are no unit outlines available online for previous years.
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