SIT789 - Robotics, Computer Vision and Speech Processing
Unit details
| Year | 2025 unit information |
|---|---|
| Enrolment modes: | Trimester 2: Waurn Ponds (Geelong), Online |
| Credit point(s): | 1 |
| EFTSL value: | 0.125 |
| Unit Chair: | Trimester 2: Duc Thanh Nguyen |
| Prerequisite: | SIT771 and SIT787. |
| Corequisite: | SIT720 |
| Incompatible with: | SIT332 |
| Educator-facilitated (scheduled) learning activities - on-campus unit enrolment: | 1 x 3 hour seminar per week. |
| Educator-facilitated (scheduled) learning activities - online unit enrolment: | Online independent and collaborative learning including 1 x 2 hour online seminar per week. |
| Typical 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. |
Content
Computer Vision and Speech Processing are fields of Artificial Intelligence that look to extract information from images, videos, and audio to better help computers interact with the real world. In this unit you will explore the application of computer vision and speech processing algorithms to solve real world problems. You will draw upon current research and state of the art tools to propose and develop novel solutions involving computer vision and speech processing.
Learning outcomes
| ULO | These are the Unit Learning Outcomes (ULOs) for this unit. At the completion of this unit, successful students can: | Alignment to Deakin Graduate Learning Outcomes (GLOs) |
|---|---|---|
| ULO1 | Draw upon current research and state-of-the-art computer vision and speech processing techniques to propose and develop novel solutions to real world problems. | GLO1: Discipline-specific knowledge and capabilities |
| ULO2 | Analyse and demonstrate robotics application based on computer vision and speech processing techniques. | GLO1: Discipline-specific knowledge and capabilities |
| ULO3 | Select and apply advanced machine learning techniques with voice, image and video data for the purpose of recognition, classification, and pattern matching. | GLO1: Discipline-specific knowledge and capabilities |
| ULO4 | Apply tools and frameworks to design, implement and evaluate computer vision and speech processing solutions. | GLO1: Discipline-specific knowledge and capabilities |
Assessment
| Assessment Description | Student output | Grading and weighting (% total mark for unit) | Indicative due week |
|---|---|---|---|
| Learning portfolio | Written and visual documentation, including program code, reports, concept maps | 100% | Week 12 |
The assessment due weeks provided may change. The Unit Chair will clarify the exact assessment requirements, including the due date, at the start of the teaching period.
Hurdle requirement
To be eligible to obtain a pass in this unit, students must meet certain milestones as part of the portfolio.
Learning resource
The texts and reading list for SIT789 can be found via the University Library.
Note: Select the relevant trimester reading list. Please note that a future teaching period's reading list may not be available until a month prior to the start of that teaching period so you may wish to use the relevant trimester's prior year reading list as a guide only.
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.
For further information regarding tuition fees, other fees and charges, invoice due dates, withdrawal dates, payment methods visit our Current Students website.