ELEC3612: Semester 1, 2025

Units ELEC3612 Semester 1 2025 [Normal day]

Unit outline_

ELEC3612: Pattern Recognition and Machine Intelligence

Semester 1, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

This unit provides a hands-on pattern recognition and machine learning course, towards solving the practical problems in computer vision and signal processing. The content of the unit is organized in a task-oriented way, including feature extraction and selection, classification, regression, outlier detection, sparse representation and dictionary learning, etc. The fundamentals of pattern recognition algorithms, such as PCA, LDA, support vector machine, ensemble, random forest, kernel methods, graphical models, etc., are delivered in the context of computer vision (such as image and video) and signal processing (such as audio, optical, and wireless signals) applications. In addition to mathematical foundations, this unit gives the students hands-on training about how to program these algorithms using python packages.

Unit details and rules

Academic unit	School of Electrical and Computer Engineering
Credit points	6
Prerequisites ?	[(MATH1X61 or MATH1971) or MATH1X02] and [(MATH1X62 or MATH1972) or (MATH1X05 or BUSS1020)]
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	1st year mathematics and 1st year Software Engineering/Electrical Engineering. Linear Algebra, Basic Programming skill
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Luping Zhou, luping.zhou@sydney.edu.au
Lecturer(s)	Charles Liu, zhenzhong.liu@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2025

Details
Criteria

Type	Description	Weight	Due	Length
Supervised exam ?	Final Exam close-book exam, 2 hours	50%	Formal exam period	2 hours
Outcomes assessed:
Tutorial quiz	Quiz 1 in-class quiz	5%	Week 05	20 minutes
Outcomes assessed:
Assignment	Project 1 Project assignment	15%	Week 07 Due date: 13 Apr 2025 at 23:59 Closing date: 14 Apr 2025	3 weeks
Outcomes assessed:
Tutorial quiz	Quiz 2 in-class quiz	5%	Week 08	20 minutes
Outcomes assessed:
Assignment	Project 2 project assignment, group work	25%	Week 12 Due date: 25 May 2025 at 23:59 Closing date: 26 May 2025	4 weeks
Outcomes assessed:
= group assignment ? = AI allowed ?

Assessment summary

Project: two project assignments, accounting to 40%
Quizzes: two in-class quizzes, accounting to 10%
Final Exam: 2 hours, accounting to 50%

Detailed information for each assessment can be found on Canvas.

Assessment criteria

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Use of generative artificial intelligence (AI) and automated writing tools

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator.

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions.

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit.

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply.

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site.

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.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

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	Independent study hours	Block teaching (81 hr)
Week 01	Introduction (Overview of statistical machine learning and its applications)	Lecture (2 hr)
Week 02	End-to-end Machine Learning	Lecture and tutorial (3 hr)
Week 03	Evaluation of the Model Performance	Lecture (3 hr)
Week 04	Supervised Learning: Regression and Classification	Lecture and tutorial (3 hr)
Week 05	Review and Quiz	Lecture and tutorial (3 hr)
Week 06	Supervised Learning: Support Vector Machines (SVM)	Lecture and tutorial (3 hr)
Week 07	Dimensionality Reduction: Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA)	Lecture and tutorial (3 hr)
Week 08	Review and Quiz	Lecture and tutorial (3 hr)
Week 09	Decision Tree 1	Lecture and tutorial (3 hr)
Week 10	Decision Tree II	Lecture and tutorial (3 hr)
Week 11	Artificial Neural Networks (ANN) I: design and activation function	Lecture and tutorial (3 hr)
Week 12	ANN II (Feedforward and Back propagation)	Lecture and tutorial (3 hr)
Week 13	Review	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. Understand the principles, algorithms, and model evaluation in pattern recognition and machine learning
LO2. Apply pattern recognition and machine learning methods to solving the practical problems in computer vision and signal processing
LO3. Master python programming for pattern recognition and gain hands-on experience
LO4. Learn to report results in professional manner
LO5. Develop some basic teamwork and project management skills through a group project

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.

This is the first time this unit has been offered.

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) and automated writing tools

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