Overview

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 … For more content click the Read More button below.

Offerings

S1-01-MALAYSIA-ON-CAMPUS
S2-01-CLAYTON-FLEXIBLE

Contacts

Chief Examiner(s)

Dr Ding Ze Yang
Associate Professor Mehrtash Tafazzoli Harandi

Unit Coordinator(s)

Dr Ding Ze Yang
Associate Professor Mehrtash Tafazzoli Harandi

Learning outcomes

On successful completion of this unit, you should 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.

Appraise critically the sources of information and contents of scientific publications and choose relevant information.

Teaching approach

Active learning
Problem-based learning
Online learning

Assessment summary

Continuous assessment: 50%

Final assessment: 50%

This unit contains threshold hurdle requirements that you must achieve to be able to pass the unit. You are required to achieve at least 45% in the total continuous assessment component and at least 45% in the final assessment component. The consequence of not achieving a hurdle requirement is a fail grade (NH) and a maximum mark of 45 for the unit.

Assessment

1 - Quizzes
2 - Assignments
3 - Laboratory assessments
4 - Final assessment

Scheduled and non-scheduled teaching activities

Studio activities
Workshops

Workload requirements

Workload

Learning resources

Required resources
Recommended resources
Technology resources

Availability in areas of study

E3001 Bachelor of Engineering (Honours) - Specialisation: Biomedical engineering
E3001 Bachelor of Engineering (Honours) - Specialisation: Robotics and mechatronics engineering (Artificial intelligence stream)
Minor: Artificial intelligence in engineering