SIT720 - Machine Learning
| Year: | 2021 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, S536J, S577, S577J, 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 week. |
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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