SIT319 - Deep Learning
| Year: | 2023 unit information |
|---|---|
| Enrolment modes: | Trimester 1: Burwood (Melbourne), Cloud (online) Trimester 2: Burwood (Melbourne), Cloud (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. |
| Scheduled learning activities - campus | 1 x 2 hour online class per week, 1 x 2 hour workshop per week. |
| Scheduled learning activities - cloud (online) | Online independent and collaborative learning including optional scheduled activities as detailed in 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.
Unit Fee Information
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