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2022 unit information
Unit delivery will be in line with the most current COVIDSafe health guidelines. We continue to tailor learning experiences for each unit to achieve the best possible mix of online and on-campus activities that successfully blend our approaches to learning, working and research. Please check your unit sites for announcements and updates.
Last updated: 4 March 2022
One of SIT718, SIT731 or SIT771For students enrolled in S464: Must have completed 16 credit points.For students enrolled in S470, S506, S507, S508, S535, S536, S538, S577, S677, S735, S737, S739, S770, S777, S778, S779, S789: Nil
Nil
Students will on average spend 150 hours over the teaching period undertaking the teaching, learning and assessment activities for this unit.
1 x 2 hour online class per week, 1 x 2 hour workshop per week. Weekly drop-in sessions.
Online independent and collaborative learning including optional scheduled activities as detailed in the unit site.
Machine learning is an important tool in analytics, where algorithms iteratively learn from data to uncover hidden insights, without being directly programmed on where to find such information. SIT720 will allow students to explore machine-learning techniques such as data representation, unsupervised learning (clustering and factor analysis) methods, supervised learning (linear and non-linear classification) methods, concepts of suitable model complexity for the problem and data at hand. Students will have the opportunity to apply these techniques in solving real-world problem scenarios presented to them in the unit.
Use Python for writing appropriate codes to solve a given problem.
GLO1: Discipline-specific knowledge and capabilitiesGLO2: CommunicationGLO3: Digital literacy
ULO2
Apply suitable clustering/dimensionality reduction techniques to perform unsupervised learning on unlabelled data in a real-world scenario.
GLO1: Discipline-specific knowledge and capabilitiesGLO3: Digital literacyGLO4: Critical thinkingGLO5: Problem solving
ULO3
Apply linear and logistic regression/classification and use model appraisal techniques to evaluate develop models.
GLO1: Discipline-specific knowledge and capabilitiesGLO4: Critical thinkingGLO5: Problem solving
ULO4
Use the concept of KNN (k-nearest neighbourhood) and SVM (support vector machine) to analyse and develop classification models for solving real-world problems.
ULO5
Apply decision tree, random forest and ensemble models to demonstrate multi-class classification models.
ULO6
Implement model selection and compute relevant evaluation measure for a given problem.
GLO1: Discipline-specific knowledge and capabilitiesGLO2: CommunicationGLO4: Critical thinking
ULO7
Use concepts of machine learning algorithms to design solution and compare multiple solutions.
GLO1: Discipline-specific knowledge and capabilitiesGLO2: CommunicationGLO5: Problem solvingGLO6: Self-management
These Unit Learning Outcomes are applicable for all teaching periods throughout the year
The assessment due weeks provided may change. The Unit Chair will clarify the exact assessment requirements, including the due date, at the start of the teaching period.
The texts and reading list for the unit can be found on the University Library via the link below: SIT720 Note: Select the relevant trimester reading list. Please note that a future teaching period's reading list may not be available until a month prior to the start of that teaching period so you may wish to use the relevant trimester's prior year reading list as a guide only.
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