CITS5508 Machine Learning

6 points
(see Timetable)
Semester 1UWA (Perth)Face to face
There is an explosion in data generation and data collection due to improvements in sensing technologies and business processes. Extracting meaningful knowledge from large amounts of data has become a priority for businesses as well as scientific domains. Data mining tools are essential for knowledge extraction from large volumes of data. In this unit, students develop in-depth understanding of data mining techniques that will be applicable both for scientific and business data. The topics include association rule mining or, market basket analysis; unsupervised machine learning techniques like k-means clustering, density based clustering, hierarchical clustering and subspace clustering; supervised machine learning techniques including artificial neural networks, decision trees, support vector machines and Bayesian belief networks.
Students are able to (1) understand the role of data mining in knowledge extraction; (2) identify appropriate data mining techniques for a given problem; (3) understand the difference between supervised and unsupervised learning algorithms; (4) understand the specific details of individual data mining algorithms; (5) implement a data mining solution for a real-world dataset; and (6) develop the ability to analyse large datasets from the perspective of data mining.
Indicative assessments in this unit are as follows: (1) mid-semester test; (2) assessed laboratory exercises; and (3) final exam. Further information is available in the unit outline.

Supplementary assessment is only available in this unit in the case of a student who has obtained a mark of 45 to 49 and is currently enrolled in this unit, and it is the only remaining unit that the student must pass in order to complete their course.
Unit Coordinator(s)
Dr Du Huynh
Unit rules
enrolment in one of the following: Master of Data Science
Master of Information Technology, and 12 points of programming-based units.
Contact hours
lectures: 2 hours per week; labs: 2 hours per week for 11 weeks from week 2
Unit Outline
Semester 1 [SEM-1]
  • The availability of units in Semester 1, 2, etc. was correct at the time of publication but may be subject to change.
  • All students are responsible for identifying when they need assistance to improve their academic learning, research, English language and numeracy skills; seeking out the services and resources available to help them; and applying what they learn. Students are encouraged to register for free online support through GETSmart; to help themselves to the extensive range of resources on UWA's STUDYSmarter website; and to participate in WRITESmart and (ma+hs)Smart drop-ins and workshops.
  • Books and other material wherever listed may be subject to change. Book lists relating to 'Preliminary reading', 'Recommended reading' and 'Textbooks' are, in most cases, available at the University Co-operative Bookshop (from early January) and appropriate administrative offices for students to consult. Where texts are listed in the unit description above, an asterisk (*) indicates that the book is available in paperback.