Part A: Course Overview

Course Title: Intelligent Systems

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

EEET2667

City Campus

Postgraduate

172H School of Engineering

Face-to-Face

 Sem 1 2025

Course Coordinator: Associate Professor Mahdi Jalili

Course Coordinator Phone: +61 3 9925

Course Coordinator Email: mahdi.jalili@rmit.edu.au


Pre-requisite Courses and Assumed Knowledge and Capabilities

Assumed Knowledge

Basic knowledge of Advanced Calculus and Linear Algebra.

Ability to use one computational language is essential (e.g. Matlab, or equivalent).


Course Description

This course will introduce students to some of the fast growing and fascinating research areas in Intelligent Systems technologies.

Students will gain a working knowledge of data analytics techniques, including neural networks, regression analysis, optimisation and evolutionary computation. The students will also gain some exposure to expert systems, and be capable of applying these techniques in a variety of engineering applications.

The objectives of the course are:

  • In-depth understanding of specialist bodies of knowledge within the engineering discipline
  • Application of established engineering methods to complex engineering problem solving
  • Fluent application of engineering techniques, tools and resources to solve engineering challenges 
  • Gain basic understanding of the underlying principles and philosophy of computational intelligence systems technologies
  • Be capable of modelling engineering systems with data analytics technologies
  • Be capable of correctly identifying existing analytics techniques for engineering applications
  • Engineering problem modelling and solving through the relationship between theoretical, mathematical, and computational modelling for predicting and optimising performance and objective
  • Be capable of constructing intelligent systems that perform useful engineering tasks


Objectives/Learning Outcomes/Capability Development

Program Learning Outcomes 

This course contributes to the program learning outcomes for the following program:  

MC235P25 - Master of Engineering (Electrical Engineering)

PLO 1. Demonstrate an advanced and integrated  understanding  of engineering theories, principles and concepts within multi-disciplinary  engineering practice ​
PLO 2. Demonstrate an advanced and integrated understanding of specialist bodies of knowledge within the engineering discipline
PLO 4. Apply advanced knowledge of established engineering methods in the analysis of complex problems in the engineering discipline
PLO 5. Utilise advanced mathematics, software, tools and techniques, in the conduct of research into the design and analysis of complex engineering systems
PLO 6. Use a systems engineering approach to synthesize and apply procedures for design, prototyping and testing to develop creative, sustainable and integrated solutions to complex engineering problems.
PLO 7. Apply advanced contemporary engineering technologies and practices and research principles and methods, taking into account risk and economic, social, environmental and global context, to plan and execute complex projects.


Upon successful completion of this course, you will be able to: 

CLO1 Apply advanced data mining and intelligent systems techniques in complex engineering problems.
CLO2 Mathematically model intelligent engineering system dynamics based on available data and data analytics.
CLO3 Utilise advanced knowledge and skills in intelligent systems for problem solving.
CLO4 Communicate an engineering intelligent system to a professional audience.


Overview of Learning Activities

The course activities include pre-recorded lectures to understanding basic concepts and principles, computer laboratory modelling tutorials, presentations, group discussions, assignments on mathematical analysis and computational implementation and reports on case studies.


Overview of Learning Resources

Course-related resources will be provided on Canvas, which includes pre-recorded lecture material, supplementary course notes, problem sheets and solutions, and useful references.

There are also many good reference books at the Library which can be used.


Overview of Assessment

Assessment 1: Mid-semester test, 15%, CLO1, CLO2 and CLO3
Assessment 2: Assignments - Two Assignments (mathematical modelling and numerical simulation), 40% (20% each), CLO1, CLO2 and CLO3
Assessment 3: End of semester test, 15%, CLO1, CLO2 and CLO3
Assessment 4: Final project report, 30%, CLO3 and CLO4

If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.