Studying online

There are now 3 possible online modes for units:

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

Units available in Online Restricted mode have been adapted for online study only for those students who require the unit to complete their studies and who are unable to attend campus due to COVID border closures. To be enrolled in a unit in Online Restricted mode, students should contact their Student Advising Office through askUWA and include which of the below criteria applies:

  • You are a student who is currently offshore and unable to enter Australia.
  • You are a student in Australia who is impacted by state or regional border closures.

Click on an offering mode for more details.

CITS5017 Deep Learning

6 points
AvailabilityLocationModeFirst year of offer
Not available in 2021UWA (Perth)Face to face
This unit focuses on advanced deep learning concepts and their application. Assuming basic machine and deep 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 in detail advanced topics in Generative Adversarial Networks (GANs), variational autoencoders, deep reinforcement learning, policy gradient methods, and Adversarial Machine Learning. These topics are covered with hands-on experience throughout the unit.
Students are able to (1) apply deep neural networks to structured and unstructured data; (2) apply generative adversarial networks to learn data distribution; (3) analyse generative models for cross-domain data generation; (4) demonstrate understanding of reinforcement learning; (5) compute adversarial patterns for deep models; and (6) contrast robust deep models with non-robust models.
Indicative assessments in this unit are as follows: (1) laboratory assessments and (2) final examination. Further information is available in the unit outline.

Supplementary assessment is not available in this unit.
Unit Coordinator(s)
Associate Professor Ajmal Saeed Mian and Dr Naveed Akhtar
Unit rules
enrolment in the BH008 Bachelor of Advanced Computer Science [Honours] (Artificial Intelligence major) and
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.