Unit outline_

# ELEC3612: Pattern Recognition and Machine Intelligence

## 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 6 [(MATH1X61 or MATH1971) OR MATH1X02] AND [(MATH1X62 or MATH1972) OR (MATH1X05 or BUSS1020)] None None 1st year mathematics and 1st year Software Engineering/Electrical Engineering. Linear Algebra, Basic Programming skill Yes

### Teaching staff

Coordinator Luping Zhou, luping.zhou@sydney.edu.au Charles Liu Maxwell Yang

## Assessment

The census date for this unit availability is 2 April 2024
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: 14 Apr 2024 at 23:59

Closing date: 15 Apr 2024
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: 19 May 2024 at 23:59

Closing date: 20 May 2024
4 weeks
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.

### 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.

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.

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

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

## 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 2023 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 2023. 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)

## Weekly schedule

WK Topic Learning activity Learning outcomes
Week 01 Introduction (Overview of statistical machine learning and its applications) Lecture (2 hr)
Week 02 End-to-end Machine Learning (Part I) Lecture and tutorial (2 hr)
Week 03 End-to-end Machine Learning (Part II) Lecture (2 hr)
Week 04 Supervised Learning: Regression and Classification Lecture and tutorial (3 hr)
Week 05 Supervised Learning: Support Vector Machines (SVM) Lecture and tutorial (3 hr)
Week 06 Dimensionality Reduction: Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) Lecture and tutorial (3 hr)
Week 07 Guest Lecture Lecture and tutorial (3 hr)
Week 08 Quiz Lecture and tutorial (3 hr)
Week 10 Desicion Tree (Modeling and Feature Selection) Lecture and tutorial (3 hr)
Week 11 Neural Networks: Perceptron, Multi-layer Perceptron, and backpropagation algorithm Lecture and tutorial (3 hr)
Week 12 Applications in computer vision and signal processing 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.

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

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.