Studying online

There are now 2 possible online modes for units:

Units with modes Online timetabled and Online flexible are available for any student to self-enrol and study online.

Click on an offering mode for more details.

Unit Overview

Description

This unit focuses on deep learning concepts and their applications. Assuming basic machine learning knowledge, and experience in related programming frameworks, it will delve deeper into the building blocks of modern deep learning systems and their specialised applications in processing structured and unstructured data. The unit covers fundamental neural architectures, including feedforward neural networks, convolutional neural networks, recurrent neural networks, gated recurrent units, long-short term memory and autoencoders, with practical examples on applications to vision and language data. These will build foundational understandings to advanced topics in variational autoencoders, Generative Adversarial Networks (GANs), transformers, deep reinforcement learning, and policy gradient methods.

Credit
6 points
Offering
(see Timetable)
AvailabilityLocationMode
Semester 2UWA (Perth)Face to face
Outcomes

Students are able to (1) demonstrate efficacy in applying deep neural networks on structured and unstructured data; (2) demonstrate understanding of autoencoders for semi-supervised learning and dimensionality reduction; (3) apply sequence processing methods to time-series data; (4) apply generative models for data generation; (5) apply generative adversarial networks to learn data distribution; and (6) demonstrate understanding of deep reinforcement learning.

Assessment

Indicative assessments in this unit are as follows: (1) mid-semester test; (2) final examination; and (3) practical projects. Further information is available in the unit outline.



Student may be offered supplementary assessment in this unit if they meet the eligibility criteria.

Unit Coordinator(s)
Associate Professor Du Huynh
Unit rules
Prerequisites
Successful completion of
CITS5508 Machine Learning
Contact hours
lectures: 2 hours per week
laboratories: 2 hours per week.
  • The availability of units in Semester 1, 2, etc. was correct at the time of publication but may be subject to change.
  • All students are responsible for identifying when they need assistance to improve their academic learning, research, English language and numeracy skills; seeking out the services and resources available to help them; and applying what they learn. Students are encouraged to register for free online support through GETSmart; to help themselves to the extensive range of resources on UWA's STUDYSmarter website; and to participate in WRITESmart and (ma+hs)Smart drop-ins and workshops.
  • Unit readings, including any essential textbooks, are listed in the unit outline for each unit, one week prior the commencement of study. The unit outline will be available via the LMS and the UWA Handbook one week prior the commencement of study. Reading lists and essential textbooks are subject to change each semester. Information on essential textbooks will also be made available on the Essential Textbooks. This website is updated regularly in the lead up to semester so content may change. It is recommended that students purchase essential textbooks for convenience due to the frequency with which they will be required during the unit. A limited number of textbooks will be made available from the Library in print and will also be made available online wherever possible. Essential textbooks can be purchased from the commercial vendors to secure the best deal. The Student Guild can provide assistance on where to purchase books if required. Books can be purchased second hand at the Guild Secondhand bookshop (second floor, Guild Village), which is located on campus.
  • Contact hours provide an indication of the type and extent of in-class activities this unit may contain. The total amount of student work (including contact hours, assessment time, and self-study) will approximate 150 hours per 6 credit points.