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Unit of study_

ELEC3612: Pattern Recognition and Machine Intelligence

Semester 1, 2022 [Normal day] - Remote

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

Unit code ELEC3612
Academic unit Electrical and Information Engineering
Credit points 6
Prohibitions
? 
None
Prerequisites
? 
MATH1002 or MATH1005
Corequisites
? 
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
Type Description Weight Due Length
Final exam (Open book) Type C final exam Final Exam
Open-book, 2 hours
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3
Tutorial quiz Quiz 1
in-class quiz
5% Week 05 20 minutes
Outcomes assessed: LO1 LO2 LO3
Assignment group assignment Project 1
Project assignment
15% Week 07
Due date: 10 Apr 2022 at 23:00

Closing date: 11 Apr 2022
3 weeks
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
Tutorial quiz Quiz 2
in-class quiz
5% Week 08 20 minutes
Outcomes assessed: LO1 LO2 LO3
Assignment group assignment Project 2
project assignment, group work
25% Week 12
Due date: 22 May 2022 at 23:00

Closing date: 23 May 2022
4 weeks
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
group assignment = group assignment ?
Type C final exam = Type C final exam ?

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.

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.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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.

WK Topic Learning activity Learning outcomes
Week 01 Introduction (Overview of statistical machine learning and its applications) Lecture (2 hr) LO1
Week 02 Python for Machine Learning Lecture (2 hr) LO1 LO3
Week 03 Supervised Learning: Regression Lecture and tutorial (2 hr) LO1 LO2 LO3
Week 04 Supervised Learning: Classification Lecture and tutorial (3 hr) LO1 LO2 LO3
Week 05 Supervised Learning: Support Vector Machines (SVM) and Decision Trees (DT) Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 06 Techniques to Improve Classification (ensemble, boosting, random) Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 07 Neural Networks: Perceptron, Multi-layer Perceptron, and backpropagation algorithm Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 08 Quiz Lecture and tutorial (3 hr) LO1 LO2 LO3
Week 09 Dimensionality Reduction: Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Autoencoders Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 10 Working with unlabeled data - Outlier Detection Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 11 Applications in computer vision and signal processing Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 12 Guest Lecture Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 13 Review Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5

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 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.

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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.