SIT343 - Feature Generation and Engineering
| Year: | 2024 unit information |
|---|---|
| Enrolment modes: | Trimester 2: Burwood (Melbourne), Online |
| Credit point(s): | 1 |
| EFTSL value: | 0.125 |
| Prerequisite: | SIT232 and SIT220 |
| 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. This will include educator guided online learning activities within the unit site. |
| Scheduled learning activities - campus | 1 x 3 hour seminar per week |
| Scheduled learning activities - online | Online independent and collaborative learning including 1 x 2 hour online seminar per week, weekly meetings. |
Content
This unit will equip students with the knowledge and skills to identify and generate features from different raw data inputs (text, image, video etc.) or signals (accelerometer, electrocardiogram, financial time series) to build machine learning models. It will also cover topics including feature transformation, creation, and selections, all these topics are directly involved with classical machine learning techniques and important to build robust and accurate models in data science.
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
Fees and charges vary depending on the type of fee place you hold, your course, your commencement year, the units you choose to study and their study discipline, and your study load.
Tuition fees increase at the beginning of each calendar year and all fees quoted are in Australian dollars ($AUD). Tuition fees do not include textbooks, computer equipment or software, other equipment or costs such as mandatory checks, travel and stationery.
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