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Machine learning (ML) is the algorithmic approach to learning from data.  This course provides an introduction to core ideas and techniques in ML, covering theoretical foundations, algorithms, and practical methodology.  Algorithms for supervised and unsupervised learning are covered, including regression, classification, neural networks, tree learning, kernel methods, clustering, dimensionality reduction, ensemble methods, and large-scale ML. Students will be given hands-on experience on applying ML algorithms to real problems and datasets.

Study Level


Offering Terms

Term 1, Term 2



Delivery Mode

Fully on-site

Indicative contact hours


Conditions for Enrolment

Course Outline

To access course outline, please visit:


Pre-2019 Handbook Editions

Access past handbook editions (2018 and prior)

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