SIT319 - Deep Learning
| Year: | 2024 unit information |
|---|---|
| Enrolment modes: | Trimester 1: Burwood (Melbourne), Online Trimester 2: Burwood (Melbourne), Online |
| Credit point(s): | 1 |
| EFTSL value: | 0.125 |
| Prerequisite: | SIT292, and one of SIT114 or SIT307 |
| Corequisite: | Nil |
| Incompatible with: | SIT744 |
| 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
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.
Unit Fee Information
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