Data Mining and Machine Learning
Subject code: MA5832:03
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.
Software platform: 
Learning outcomes
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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
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Describe, choose, and apply unsupervised machine learning methods for descriptive data mining tasks, such as clustering and outlier detection
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Describe, choose, and apply supervised techniques for dimensionality reduction via feature selection
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Describe, choose, and apply semi-supervised and/or supervised machine learning methods for predictive data mining tasks, such as pattern classification and regression
Assessment
Assessment for this course will occur at various times across the seven-week study period. Tasks may include online quizzes, discussion board activity, portfolio development, case studies, reflection, literature reviews presentations and reports.Feedback will be provided to you throughout the study period as well as a final grade at the conclusion of the study period. Click here to find out more about this subject's assessments.
This is one of the interdisciplinary subjects studied in the online Master of Data Science.
Please note, course structure and content are subject to change. For information on all course subjects download the course guide.
