TRC4901 - Artificial intelligence for engineers - 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 Mechanical and Aerospace Engineering

Chief examiner(s)

Professor Chris Davies


Dr S Parasuraman (Malaysia)

Unit guides



  • First semester 2019 (On-campus)




ECE4708, ECE5708, GSE4703


This unit provides in-depth study of the topics in common engineering, such as knowledge representation, reasoning, learning by experience and evolutionary system that serves as the foundations of artificial intelligence, declarative programming and the design of any intelligent system. Topic covers theory, design problem and applications of those Knowledge representation and reasoning using Fuzzy Logic System, Fuzzy clustering, Neuro-Fuzzy Modelling; Machine Learning in ANNs such as Supervised, Unsupervised and reinforcement learning strategies with various algorithms; Introduction to Deep Learning, Techniques & Applications and Evolutionary computation such as Optimisation algorithms, Simulated annealing and Genetic algorithms. Modern tools will be used to evaluate the above AI techniques and synthesise solutions to practical examples.


  • To design and develop a fuzzy knowledge-based intelligent machine using the human sense of linguistic & decision-making.
  • To design, build and train the Artificial Neural Network to solve a variety of multi-disciplinary engineering and business problems.
  • To recap the differences between machine and deep learning techniques and to solve big data in a variety of application using deep learning techniques.
  • To evaluate, synthesise and predict solutions using above AI techniques using Modern tools.


Continuous assessment: 60%

Final Examination (2 hours): 40%

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 of lectures, 2 slots of 2 hours practice classes and 6 hours week of private study by the student

See also Unit timetable information