ECE4179 - Neural networks and deep learning - 2019

6 points, SCA Band 2, 0.125 EFTSL

Undergraduate - Unit

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.



Organisational Unit

Department of Electrical and Computer Systems Engineering

Chief examiner(s)

Professor Tom Drummond


Dr Mehrtash Harandi

Unit guides



  • Second semester 2019 (On-campus)


(ENG2005 or ENG2092) and ECE2071


This unit introduces fundamentals of deep learning and how it can solve problems in many areas, such as image classification, filter design and natural language processing. Neural networks are first described and how training can be achieved with backpropagation. Various forms of deep neural networks are developed, such as multilayer perceptrons, convolution neural networks and recurrent neural networks. The mathematics of stochastic optimisation is used to interpret and understand the behaviour and training of these networks. Programming approaches are discussed for training and deploying neural networks. Deep learning technologies and design examples are discussed in areas such as driverless cars, personal cognitive assistants and mastering of games such as GO.


On successful completion of this unit, students will be able to:

  1. Describe concepts and fundamentals of deep learning, such as the backpropagation algorithm and adversarial learning.
  2. Discern and appreciate various forms of deep neural networks, such as multilayer perceptrons, convolution neural networks and recurrent neural networks.
  3. Interpret and apply the mathematics of deep learning, such as stochastic optimisation.
  4. Design deep learning solutions to problems in computer vision, natural language processing and signal processing. Examples are image classification, object detection, sequence modelling and filter design.
  5. Demonstrate the training and deployment of neural networks using a high level programming language.
  6. Critically appraise sources of information and contents of scientific publications and choose relevant information.


NOTE: From 1 July 2019, the duration of all exams is changing to combine reading and writing time. The new exam duration for this unit is 2 hours and 10 minutes.

Continuous assessment: 50%

Examination (2 hours): 50%

Students are required to achieve at least 45% in the total continuous assessment component and at least 45% in the final examination component and an overall mark of 50% to achieve a pass grade in the unit. Students failing to achieve this requirement will be given a maximum of 45% in the unit.

Workload requirements

2 hours lectures, 2 hours of laboratory classes, 1 hour practice class and 7 hours of private study per week.

See also Unit timetable information