FIT3181 Deep learning
Faculty of Information Technology
FIT3181 Deep learning is a level 3, 6-credit-point, undergraduate unit from the Faculty of Information Technology, offered in 2021 in Semester 2 at Clayton and Malaysia. It needs FIT2086.
- Credit points
- 6
- Offered in 2021
- Semester 2
- Clayton, Malaysia
- Assessment
- No exam
- 5 tasks
- Workload
- 144 hours
- per semester
This is the 2021 handbook entry. See the 2027 entry.
Reviews
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Requisites
Before FIT3181
Prerequisites
Pass these before you enrol.
After FIT3181
No unit lists FIT3181 as a prerequisite in the 2021 handbook.
Overview
Modern machine learning provides core underlying theory and techniques to data science and artificial intelligence. This unit is for students to develop practical knowledge of modern machine learning and deep learning and how they can be used in real-world settings such as image recognition or text clustering via neural embeddings. Learning activities will focus on designing machine learning systems, a broad landscape of supervised and unsupervised learning methods with a focus on modern deep learning knowledge for data analytics including deep neural networks, representation learning and embedding methods, and deep models used for time-series data which are rapidly used in science and industry.
Offerings in 2021
| Teaching period | Campus | Mode |
|---|---|---|
| Second semester | Clayton | On campus |
| Second semester | Malaysia | On campus |
Assessment
- Assignment 1: Deep learning knowledge and programming tasksThreshold hurdle20%
- In-semester assessment 1: machine learning and deep learning knowledge testThreshold hurdle10%
- Assignment 2: Advanced knowledge and programming tasksThreshold hurdle20%
- In-semester Assessment 2Threshold hurdle10%
- Scheduled final assessmentThreshold hurdle40%
Learning outcomes
When you finish this unit, you should be able to:
- 1
Evaluate the life cycle of a machine leaning system, what is involved in designing such systems and strategy to maintain them.
- 2
Assess what deep learning (DL) is, what makes DL work or fail, and critique where they should be applied.
- 3
Construct and apply deep neural networks, deep generative models and different optimization strategies for training them.
- 4
Develop unsupervised feature learning models and representation learning models.
Workload and teaching
- Laboratories24 hours
- Lectures24 hours
- Teaching approachActive learning
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled activities. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning and online engagement.
Where it fits
FIT3181 is part of 3 areas of study in the 2021 handbook.
Contacts
- Unit Coordinators
- Dr Lim Chern Hong
- Chief Examiners
- Dr Trung Le
Common questions
What are the prerequisites for FIT3181?
You need FIT2086 before you enrol.
When is FIT3181 offered?
In 2021, FIT3181 runs in Semester 2 at Clayton and Malaysia.
How much work is FIT3181?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does FIT3181 have an exam?
No. FIT3181 has 5 assessment tasks and no exam.
Which majors and minors include FIT3181?
FIT3181 is part of Advanced computer science, Computational science and Data science.