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MA5832 - Data Mining and Machine Learning

Credit points: 3
Year: 2022
Student Contribution Band: Band 1
Administered by: College of Science and Engineering

This subject will provide students with a range of algorithms based on machine learning techniques for advanced data analysis and mining. These algorithms and techniques fall within the most common machine learning paradigms, namely, unsupervised, semi-supervised, and supervised learning. In particular, students will learn sophisticate machine learning methods for clustering, outlier detection, classification, feature selection, and regression.

Learning Outcomes

  • explain what machine learning for data mining is about and identify the most common tasks and roles of machine learning in the realm of data mining;
  • describe, choose, and apply unsupervised machine learning methods for descriptive data mining tasks, such as clustering and outlier detection;
  • describe, choose, and apply supervised techniques for dimensionality reduction via feature selection;
  • describe, choose, and apply semi-supervised and/or supervised machine learning methods for predictive data mining tasks, such as pattern classification and regression.

Subject Assessment

  • Written > Problem task - (60%) - Individual
  • Written > Project report - (40%) - Individual.
Prerequisites: MA5810 AND 24CP OF POSTGRADUATE SUBJECTS

Availabilities

Cairns, Study Period 83, Internal
Census Date 19-May-2022
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 26 hours tutorials
  • assessment and self-directed study
Restrictions: Enrolment in this offering is restricted.

JCU Online, Study Period 83, External
Census Date 19-May-2022
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 65 hours - Online resources including readings, screencasts, embedded quizzing
  • assessment and self-directed study
Method of Delivery: Online - JCU
Restrictions: Enrolment in this offering is restricted.

Note: Minor variations might occur due to the continuous Subject quality improvement process, and in case of minor variation(s) in assessment details, the Subject Outline represents the latest official information.