SIT307 - Machine Learning

Year:

2023 unit information

Enrolment modes: Trimester 1: Burwood (Melbourne), Cloud (online)
Trimester 2: Burwood (Melbourne), Cloud (online)
Credit point(s): 1
Previously coded as:

SIT372

EFTSL value: 0.125
Assumed knowledge:

Knowledge of basic statistics is recommended

Prerequisite:

SIT232 and one unit from SIT112, SIT114, SIT191, SIT199

Corequisite:

Nil

Incompatible with:

Nil

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 2 hour online class per week, 1 x 2 hour workshop per week. Weekly drop in sessions.

Scheduled learning activities - cloud (online)

Online independent and collaborative learning including optional scheduled activities as detailed in the unit site.

Content

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. This unit involves students exploring 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.

Hurdle requirement

To be eligible to pass the unit, students must pass certain milestones in the portfolio.

Unit Fee Information

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