Course Title: Intelligent Systems
Part A: Course Overview
Course Title: Intelligent Systems
Credit Points: 12.00
Terms
Course Code |
Campus |
Career |
School |
Learning Mode |
Teaching Period(s) |
EEET2171 |
City Campus |
Undergraduate |
125H Electrical & Computer Engineering |
Face-to-Face |
Sem 2 2006, Sem 2 2007, Sem 2 2008 |
EEET2171 |
City Campus |
Undergraduate |
172H School of Engineering |
Face-to-Face |
Sem 1 2021, Sem 1 2022, Sem 1 2023, Sem 1 2024 |
EEET2316 |
City Campus |
Postgraduate |
125H Electrical & Computer Engineering |
Face-to-Face |
Sem 2 2008 |
EEET2316 |
City Campus |
Postgraduate |
172H School of Engineering |
Face-to-Face |
Sem 1 2020, Sem 1 2021, Sem 1 2022, Sem 1 2023, Sem 1 2024 |
Flexible Terms
Course Code |
Campus |
Career |
School |
Learning Mode |
Teaching Period(s) |
EEET2629 |
SHAPE, VTC |
Undergraduate |
172H School of Engineering |
Face-to-Face |
OFFMay2024 (All) |
Course Coordinator: Associate Professor Mahdi Jalili
Course Coordinator Phone: +61 3 9925 1223
Course Coordinator Email: mahdi.jalili@rmit.edu.au
Pre-requisite Courses and Assumed Knowledge and Capabilities
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
The learning outcomes will enable you to develop the following capabilities:
- Apply critical and creative thinking in the design of engineering projects.
- Apply knowledge of the ‘real world’ situations that a professional engineer can encounter
- Utilise fundamental knowledge and skills in engineering and apply it effectively to a project
- Present your work to your peers and academics by create system reports and system specification documents within the simulation environment
- Effectively use data analytics technologies to solve engineering problems
- Apply control mechanism and management function to ensure that the system achieve its purpose
On successful completion of this course you should be able to:
- Ability to model engineering systems based on available data
- Ability to effectively apply available data analytics techniques to engineering problems
- Awareness of knowledge development and research directions within the engineering discipline.
- Ability to understand system dynamics and correctly chose analytics technique
- Ability to estimate and validate a linear/nonlinear model based upon input and output data from a system.
- Ability to plan and execute research-based assessment tasks, with creativity and initiative in new situations in professional practice and with a high level of personal autonomy and accountability.
- Ability to identify engineering challenges and develop creative and innovative solutions to solve them.
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
This course has no hurdle requirements.
The purpose of assessments is to determine whether you have acquired aimed capabilities.
Assignments are designed based on the theory developed in this course.
Assessment 1: Mid-semester test
Weighting 15%
CLOs 1, 2, 4 & 5
Assessment 2: Assignments - Two Assignments (mathematical modelling and numerical simulation)
Weighting 40% (20% each)
CLOs 1, 2, 4 & 5
Assessment 3: End of semester test
Weighting 15%
CLOs 1, 2, 4 & 5
Assessment 4: Final project report
Weighting 30%
CLOs 1,2,3,6 &7
ASSESSMENT CRITERIA:
You are requested to complete the assignments by YOURSELF. However, it does not prevent you from discussing with your fellow students about any aspect of the assignments. YOU MUST ACKNOWLEDGE ALL SOURCES, notes, texts and colleagues that are used and consulted in your assignments. We reserve the right to penalise any plagiarism. You must submit your assignment work by the due date.