SIT744 - Deep Learning
Unit details
| Year | 2025 unit information |
|---|---|
| Enrolment modes: | Trimester 1: Burwood (Melbourne), Online Trimester 2: Burwood (Melbourne), Waurn Ponds (Geelong), Online |
| Credit point(s): | 1 |
| EFTSL value: | 0.125 |
| Unit Chair: | Trimester 1: Wei Luo Trimester 2: Thommen Karimpanal |
| Prerequisite: | SIT720 or SIT742 |
| Corequisite: | Nil |
| Incompatible with: | SIT319 |
| Educator-facilitated (scheduled) learning activities - on-campus unit enrolment: | 1 x 2 hour online lecture per week, 1 x 2 hour practical experience (workshop) per week, weekly meetings. |
| Educator-facilitated (scheduled) learning activities - online unit enrolment: | Online independent and collaborative learning including 1 x 2 hour online lecture per week (recordings provided), 1 x 2 hour practical experience (workshop) per week, weekly meetings. |
| 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
Deep learning is a disruptive technology for data science and artificial intelligence. This unit is for students to develop practical knowledge of deep learning and associated applications. Learning activities will focus on understanding deep learning theories, constructing deep learning models for handling structured and unstructured data, such as images, videos, and texts. Concepts such as computational graphs and representation learning that form core knowledge in this unit will be introduced. Students will also learn about deep learning techniques for data analytics such as convolutional networks, recurrent networks, and neural embedding methods which are being widely adopted in industries.
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 | Explain deep learning and its role in data science and AI. | GLO1: Discipline-specific knowledge and capabilities |
| ULO2 | Apply deep learning theory to formulate data analytics or artificial intelligence problems. | GLO1: Discipline-specific knowledge and capabilities |
| ULO3 | Design suitable deep learning algorithms for unsupervised learning and supervised learning problems. | GLO1: Discipline-specific knowledge and capabilities |
| ULO4 | Model and implement algorithms for processing structured and unstructured data, including images videos, and texts. | GLO1: Discipline-specific knowledge and capabilities |
Assessment
| Assessment Description | Student output | Grading and weighting (% total mark for unit) | Indicative due week |
|---|---|---|---|
| Assessment 1 | Written answers, program source codes and outputs | 20% | Week 3 |
| Assessment 2 | Written answers, program source codes and outputs | 30% | Week 6 |
| Assessment 3 Problem solving task | Written answers, program source codes and outputs | 30% | Week 9 |
| Assessment 4 Problem solving task | Written answers, program source codes and outputs | 20% | Week 11 |
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.
Learning resource
The texts and reading list for SIT744 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.