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SIT720 - Machine Learning


2020 unit information

Important Update:

Classes and seminars in Trimester 2/Semester 2, 2020 will be online. Physical distancing for coronavirus (COVID-19) will affect delivery of other learning experiences in this unit. Please check your unit sites for announcements and updates one week prior to the start of your trimester or semester.

Last updated: 2 June 2020

Enrolment modes:Trimester 2: Burwood (Melbourne), Waurn Ponds (Geelong), Cloud (online)
Credit point(s):1
EFTSL value:0.125
Unit Chair:Trimester 2: Chandan Karmakar

SIT718 or SIT771

For students enrolled in S536, S577, S737: Nil


Incompatible with:


Typical study commitment:

Students will on average spend 150 hours over the teaching period undertaking the teaching, learning and assessment activities for this unit.

Scheduled learning activities - campus:

1 x 1 hour class per week, 1 x 1 hour workshop per week.

Scheduled learning activities - cloud:

1 x 1 hour of scheduled online seminar per fortnight.


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.


These are the Learning Outcomes (ULO) for this Unit

At the completion of this Unit
successful students can:

Deakin Graduate Learning Outcomes


Use Python for writing appropriate codes to solve a given problem.

GLO1: Discipline-specific knowledge and capabilities
GLO2: Communication
GLO3: Digital literacy


Apply suitable clustering/dimensionality reduction techniques to perform unsupervised learning on unlabelled data in a real-world scenario.

GLO1: Discipline-specific knowledge and capabilities
GLO3: Digital literacy
GLO4: Critical thinking
GLO5: Problem solving


Apply linear and logistic regression/classification and use model appraisal techniques to evaluate develop models.

GLO1: Discipline-specific knowledge and capabilities
GLO4: Critical thinking
GLO5: Problem solving 



Use the concept of KNN (k-nearest neighbourhood) and SVM (support vector machine) to analyse and develop classification models for solving real-world problems.

GLO1: Discipline-specific knowledge and capabilities
GLO4: Critical thinking
GLO5: Problem solving


Apply decision tree and random forest models to demonstrate multi-class classification models.

GLO1: Discipline-specific knowledge and capabilities
GLO4: Critical thinking
GLO5: Problem solving


Implement model selection and compute relevant evaluation measure for a given problem. 

GLO1: Discipline-specific knowledge and capabilities
GLO2: Communication
GLO4: Critical thinking

These Unit Learning Outcomes are applicable for all teaching periods throughout the year


Assessment Description Student output Weighting (% total mark for unit) Indicative due week

Two online quizzes

Approximately 20 questions to answer within 45 minutes

20% (2 x 10%) Weeks 5 and 10
Problem-solving tasks  Problem-solving tasks  45% (5%, 20%, 20%) Weeks 3, 6 and 8
Project Written report, approximately 2,000 words 35% Week 11

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.

Learning Resource

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.

Unit Fee Information

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