ELEC5308: Semester 2, 2025

Units ELEC5308 Semester 2 2025 [Normal day]

Unit outline_

ELEC5308: Intelligent Information Engineering Practice

Semester 2, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

This unit aims at the practising ability of students on utilizing intelligent information engineering techniques for solving practical problems in the latest applications of AI, e.g., Autopilot. Students will get programming skills for many tasks related to automatic driving, including lane detection, traffic sign detection, pedestrian detection, and path planning. Lane detection, traffic sign detection, pedestrian detection, and path planning are information processing techniques that will help students to learn how to use the Video Intelligence and Signal Understanding approaches for many practical problems. All students will be involved in designing mini-projects and a large project. The unit is project-oriented. Students will run their programs on simulated environment. The course will be taught through lectures mainly on how to accomplish the goal for the mini-project and the final project. A specific lab design will be provided to students for hands-on design. Communication skills will be tested through several project presentations. Some teaching will be provided by intelligent information engineers working in the industry.

Unit details and rules

Academic unit	School of Electrical and Computer Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Students must have a good understanding of Linear algebra and basic mathematics, Basic Programming skills in C, Python or Matlab
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Dong Yuan, dong.yuan@sydney.edu.au

Assessment

The census date for this unit availability is 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Experimental design	Mini project Assessing the submitted code, report, and demo.	20%	Week 08	-	AI allowed
Outcomes assessed:
Out-of-class quiz	Assignment The answer consists of design and implementation.	40%	Week 11 Due date: 14 Oct 2024 at 23:59	-	AI allowed
Outcomes assessed:
Experimental design	Project Assessing the submitted code, report, and demo.	40%	Week 13	-	AI allowed
Outcomes assessed:
= group assignment ?

Assessment summary

Two group projects:

– one mini-project

– one larger project

One individual project.

The assessment approach of this unit covers both group and individual contributions, examining the technical merit, any extensive studies, completeness of documentation as well as the presentation of the solution.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

Result name	Mark range	Description
Mini project	0-100	The mini project will require students to develop some fundamental functionality in groups using the techniques covered thus far. The deliverable includes source code, software artefacts, documentation and demonstration.
Assignment	0-100	The assignment will consist of some design and implementation questions. The questions are often open-ended aiming at testing how far and wide the student has researched and studied in the related area. This assignment must be done independently. The answers are supposed to include solution designs in the form of text and graphs, (optionally) some pseudo or real code to indicate how things may work.
Final project	0-100	The final project will require students to develop more advanced functionality in groups using techniques covered in this unit and any extended studies in this area. The deliverable includes source code, software artefacts, documentation and demonstration.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

5% per day.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Multiple weeks	Review the lectures and lab notes, conduct the project with group members and write the report.	Independent study (80 hr)
Week 01	Lecture 1: Introduction and Overview. Lab: Overview	Lecture and tutorial (3 hr)
Week 02	Lecture 2: Technology review and basics of image acquisition & control. Laboratory: Image acquisition & control	Lecture and tutorial (3 hr)
Week 03	Lecture 3: Machine learning in computer vision. Laboratory: Lane detection – part 1	Lecture and tutorial (3 hr)
Week 04	Lecture 4: Practical engineering considerations. Laboratory: Lab 3 & project 1 starts	Lecture and tutorial (3 hr)
Week 05	Lecture 5: Computer vision and ICT. Laboratory: Lab 4	Lecture and tutorial (3 hr)
Week 06	Lecture 6: Machine learning and models. Laboratory: Lab 5	Lecture and tutorial (3 hr)
Week 07	Lecture 7: Reinforcement learning. Laboratory: Lab 6 and project 1 due	Lecture and tutorial (3 hr)
Week 08	Lecture 8: Deep reinforcement learning and practices Laboratory: Project 2 starts.	Lecture and tutorial (3 hr)
Week 09	Lecture 9: Learning and agent systems. Laboratory: Lab 8	Lecture and tutorial (3 hr)
Week 10	Lecture 10: DRL and multi-agent system. Laboratory: Lab 9, project 2 due and project 3 starts	Lecture and tutorial (3 hr)
Week 11	Lecture 11: Unsupervised learning and anomaly detection. Laboratory: Lab 10	Lecture and tutorial (3 hr)
Week 12	Lecture 12: Case studies and technical reviews. Laboratory: Lab 11 and project Q&A.	Lecture and tutorial (3 hr)
Week 13	Lecture 13: Unit review and demo. Laboratory: Project 3 due and demo	Lecture and tutorial (3 hr)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. To learn basic intelligent information engineering techniques
LO2. To learn how to apply intelligent information engineering techniques to practical problems on a robot.
LO3. To apply programming skills for research and application tasks related to the intelligent information and signal processing, and automatic driving.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

We take the feedback from last year's teaching and improve the content for this year.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links