SIT720 - Machine Learning
| Year: | 2024 unit information |
|---|---|
| Enrolment modes: | Trimester 1: Burwood (Melbourne), Waurn Ponds (Geelong), Online Trimester 2: Burwood (Melbourne), Waurn Ponds (Geelong), Online |
| Credit point(s): | 1 |
| EFTSL value: | 0.125 |
| Prerequisite: | One of SIT718, SIT731 or SIT771 |
| Corequisite: | Nil |
| Incompatible with: | SIT307 |
| Study commitment | Students will on average spend 150 hours over the teaching period undertaking the teaching, learning and assessment activities for this unit. This will include educator guided online learning activities within the unit site. |
| Scheduled learning activities - campus | 1 x 2 hour online lecture per week, 1 x 2 hour practical experience (workshop) per week. Weekly meetings. |
| Scheduled learning activities - online | Online independent and collaborative learning including 1 x 2 hour online lecture per week (recordings provided), 1 x 2 hour online practical experience (workshop) per week. Weekly meetings. |
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 of the portfolio.
Unit Fee Information
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