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Unit of study_

COMP4318: 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 COMP4318
Academic unit Computer Science
Credit points 6
Prerequisites:
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None
Corequisites:
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Enrolment in a thesis unit. INFO4001 or INFO4911 or INFO4991 or INFO4992 or AMME4111 or BMET4111 or CHNG4811 or CIVL4022 or ELEC4712 or COMP4103 or SOFT4103 or DATA4103 or ISYS4103
Prohibitions:
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COMP5318 OR OCMP5318
Assumed knowledge:
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Experience with programming and data structures as covered in COMP2123 or COMP2823 or COMP9123 (or equivalent UoS from different institutions)

At the completion of this unit, you should be able to:

  • LO1. LO1: understand the basic principles, strengths, weaknesses and applicability of machine learning algorithms for solving classification, regression, clustering and reinforcement learning tasks.
  • LO2. LO2: have obtained practical experience in designing, implementing and evaluating machine learning algorithms
  • LO3. LO3: have gained practical experience in using machine learning software and libraries
  • LO4. LO4: present and interpret data and information in verbal and written form

Unit outlines

Unit outlines will be available 1 week before the first day of teaching for the relevant session.

There are no unit outlines available online for previous years.