CITS5508 Machine Learning
- 6 points
|Semester 1||UWA (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.