ELEC3612: Semester 1, 2026

Units ELEC3612 Semester 1 2026 [Normal day]

Unit outline_

ELEC3612: Pattern Recognition and Machine Intelligence

Semester 1, 2026 [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	Shahadat Uddin, shahadat.uddin@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final Exam close-book exam, 2 hours	40%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Experimental design	Project 1 (Individual) Project assignment; Individual work	20%	Week 07	25 page (max)	AI allowed
Outcomes assessed:
In-person practical, skills, or performance task or test	Knowledge Test Knowledge Test	20%	Week 09	50 minutes	AI prohibited
Outcomes assessed:
Experimental design	Project 2 (Group) Project assignment; Group work	20%	Week 13	25 page (max)	AI allowed
Outcomes assessed:
= group assignment ?

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)

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.

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	Independent study hours	Self-directed learning (81 hr)
Week 01	Introduction, Overview of the Course and Data Preprocessing	Lecture (2 hr)
Week 01	Select a Dataset and apply data preprocessing techniques to make it suitable for ML analysis	Tutorial (1 hr)
Week 02	Basics of Model Development (Confusion Matrix, Hyperparameter Tuning, K-Fold CV, Performance Measure and Seeding) and SVM	Lecture (2 hr)
Week 02	Implement SVM with different ML settings	Tutorial (1 hr)
Week 03	Regression, Logistic Regression and Machine Learning	Lecture (2 hr)
Week 03	Implement LR and compare it with SVM with different settings	Tutorial (1 hr)
Week 04	Tree-based ML (DT and RF)	Lecture (2 hr)
Week 04	Implement DT and RF and compare them with the non-tree-based MLs (SVM and LR)	Tutorial (1 hr)
Week 05	Unsupervised Machine Learning (Performance Measure and KMC)	Lecture (2 hr)
Week 05	Implement KMC and its different Variants	Tutorial (1 hr)
Week 06	Ensemble Approaches (Bagging, Boosting and Stacking)	Lecture (2 hr)
Week 06	Implement and Compare Different Ensemble Approaches	Tutorial (1 hr)
Week 07	Deep Learning (ANN and CNN)	Lecture (2 hr)
Week 07	Exercise using CNN on an Image Dataset (Will Use a Different Dataset)	Tutorial (1 hr)
Week 08	Fairness in Machine Learning	Lecture (2 hr)
Week 08	Checking Fairness Violations Using Different Metrics of a Dataset	Tutorial (1 hr)
Week 09	Quiz and Review	Lecture (2 hr)
Week 09	Quiz and Review	Tutorial (1 hr)
Week 10	Principal Component Analysis and Linear Discriminant Analysis	Lecture (2 hr)
Week 10	Implement and Compare PCA and LDA Approaches on a Dataset	Tutorial (1 hr)
Week 11	Graphical ML Methods (GNN and GAT)	Lecture (2 hr)
Week 11	Implement and Compare GNN and GAT	Tutorial (1 hr)
Week 12	ML Applications in Computer Vision	Lecture (2 hr)
Week 12	Exercise Using an audio/optical/signal data	Tutorial (1 hr)
Week 13	Review and Revision	Lecture (2 hr)
Week 13	Review and Revision	Tutorial (1 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 I am offering this UoS.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

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