SIT720 - Machine Learning

Year:

2023 unit information
Enrolment modes: Trimester 1: Burwood (Melbourne), Waurn Ponds (Geelong), Cloud (online)
Trimester 2: Burwood (Melbourne), Waurn Ponds (Geelong), Cloud (online)
Credit point(s): 1
EFTSL value: 0.125
Prerequisite:

One of SIT718, SIT731 or SIT771
For 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
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 1 x 2 hour online class per week (recordings provided), 1 x 2 hour online workshop per week. Weekly drop-in sessions.

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 obtain a pass in this unit, students must meet certain milestones as part ofthe portfolio.

Unit Fee Information

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