CITS5017 Deep Learning

6 points

If this unit does not have an online alternative, then students who are presently unable to enter Western Australia and whose studies would be delayed by an inability to complete this unit, should contact the unit coordinator (details given on this page) to ascertain, on an individual case-by-case basis, if alternate arrangements can be made to support their study in this unit.

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.
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