2020 unit information
Classes and seminars in Trimester 2/Semester 2, 2020 will be online. Physical distancing for coronavirus (COVID-19) will affect delivery of other learning experiences in this unit. Please check your unit sites for announcements and updates one week prior to the start of your trimester or semester.
Last updated: 2 June 2020
SIT720 or SIT742
Students will on average spend 150 hours over the teaching period undertaking the teaching, learning and assessment activities for this unit.
1 x 2 hour class per week, 1 x 1 hour practical per week.
1 x 1 hour scheduled online workshop per week.
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.
These are the Learning Outcomes (ULO) for this Unit
At the completion of this Unitsuccessful students can:
Deakin Graduate Learning Outcomes
Explain deep learning and its role in data science and AI.
GLO1: Discipline-specific knowledge and capabilitiesGLO2: Communication
Apply deep learning theory to formulate data analytics or artificial intelligence problems.
GLO1: Discipline-specific knowledge and capabilitiesGLO4: Critical thinkingGLO5: Problem solving
Design suitable deep learning algorithms for unsupervised learning and supervised learning problems.
GLO1: Discipline-specific knowledge and capabilitiesGLO3: Digital literacyGLO4: Critical thinkingGLO5: Problem solving
Model and implement algorithms for processing structured and unstructured data, including imagesvideos, and texts.
These Unit Learning Outcomes are applicable for all teaching periods throughout the year
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.
The texts and reading list for the unit can be found on the University Library via the link below: SIT744 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.
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