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SIT789 - Applications of Computer Vision and Speech Processing

Year:

2022 unit information

Important Update:

Unit delivery will be in line with the most current COVIDSafe health guidelines. We continue to tailor learning experiences for each unit to achieve the best possible mix of online and on-campus activities that successfully blend our approaches to learning, working and research. Please check your unit sites for announcements and updates.

Last updated: 4 March 2022

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.
For students enrolled in S506, S507, S508, S535, S536, S538, S577, S677, S735, S737, S739, S770, S778, S779, S789: SIT787

Corequisite:

SIT720

Incompatible with:

Nil

Typical 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 2 hour online class per week, 1 x 2 hour workshop per week.

Scheduled learning activities - cloud:

Online independent and collaborative learning including optional scheduled activities as detailed in 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.

ULO These are the Learning Outcomes (ULO) for this unit. At the completion of this unit, successful students can: Deakin Graduate Learning Outcomes
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
GLO3: Digital literacy
GLO5: Problem solving

ULO2

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
GLO4: Critical thinking

ULO3

Apply tools and frameworks to design, implement and evaluate computer vision and speech processing solutions.

GLO1: Discipline-specific knowledge and capabilities
GLO3: Digital literacy

ULO4

Work effectively as a member of a team to develop working computer vision and speech processing systems.

GLO7: Teamwork

These Unit Learning Outcomes are applicable for all teaching periods throughout the year

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 the unit can be found on the University Library via the link below: SIT789 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

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