SIT720 - Machine Learning

Year:

2020 unit information

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

0.125

Prerequisite:

SIT718 or SIT771

For students enrolled in S536, S577, S737: 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 1 hour class per week, 1 x 1 hour workshop per week.

Scheduled learning activities - cloud (online)

1 x 1 hour of scheduled online seminar per fortnight.

Note:

This unit uses the FutureLearn online learning platform. Learn more about studying through FutureLearn.

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. 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.

Unit Fee Information

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