ELEC5307: Semester 2, 2022

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Units ELEC5307 Semester 2 2022 [Normal day]

Unit outline_

ELEC5307: Advanced Signal Processing with Deep Learning

Semester 2, 2022 [Normal day] - Remote

Overview

This unit of study introduces deep learning for a broad range of multi-dimensional signal processing applications. It covers deep learning technologies for image super-resolution and restoration, image categorization, object localization, image segmentation, face recognition, person detection and re-identification, human pose estimation, action recognition, object tracking as well as image and video captioning.

Unit details and rules

Academic unit	School of Electrical and Computer Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Mathematics (e.g., probability and linear algebra) and programming skills (e.g. Matlab/Java/Python/C++)
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Luping Zhou, luping.zhou@sydney.edu.au
Lecturer(s)	Luping Zhou, luping.zhou@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Open book)	Final exam Open-book exam for two hours	60%	Formal exam period	2 hours
Outcomes assessed:
Assignment	Project 1 Group work	20%	Week 08 Due date: 25 Sep 2022 at 23:59	n/a
Outcomes assessed:
Assignment	Project 2 Group work	20%	Week 11	n/a
Outcomes assessed:
= Type C final exam ?

Assessment summary

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

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.

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to deep learning (e.g., historical review of machine learning and deep learning, basic machine learning concepts, performance evaluation)	Lecture (2 hr)
Week 02	Lecture: Basics of Neural Networks; Tutorial 1: Python Basics	Lecture and tutorial (3 hr)
Week 03	Lecture: CNN Architectures; Tutorial 2: Python Packages	Lecture and tutorial (3 hr)
Week 04	Lecture: CNN Applications (I); Tutorial 3: DataLoader in PyTorch	Lecture and tutorial (3 hr)
Week 05	Lecture: Practical in Neural Network; Tutorial 4: Forward Process in PyTorch	Lecture and tutorial (3 hr)
Week 06	Lecture: Graphical Model and RNN; Tutorial 5: Backward Process in PyTorch	Lecture and tutorial (3 hr)
Week 07	Lecture: Deep Generative Models; Project 1: Parameters in Neural Networks	Lecture and tutorial (3 hr)
Week 08	Lecture: Invited Guest Lecture; Project 1: Parameters in Neural Networks (continue)	Lecture and tutorial (3 hr)
Week 09	Lecture: Applications (II); Project 2: Classification Challenge (data release and start)	Lecture and tutorial (3 hr)
Week 10	Lecture: Attention and Transformer; Project 2: Classification Challenge (continue)	Lecture and tutorial (3 hr)
Week 11	Lecture: Learning with Few Labels; Project 2: Classification Challenge (continue) and due	Lecture and tutorial (3 hr)
Week 12	Lecture: Invited Guest Lectures; Project 2: Classification Challenge Presentation	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.

Required readings

All readings for this unit can be accessed through the Library eReserve, available on Canvas.

I. Goodfellow, Y. Bengio and A. Courville, Deep Learning. MIT Press, 2016.

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. use appropriate software platforms for a given multi-dimensional signal processing task
LO2. understand and apply the machine learning and deep learning methods for multi-dimensional signal processing applications
LO3. use the existing machine learning and deep learning toolboxes
LO4. report results in a 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

Alignment with Competency standards

Outcomes	Competency standards
	Engineers Australia Curriculum Performance Indicators - 1.1. Developing underpinning capabilities in mathematics, physical, life and information sciences and engineering sciences, as appropriate to the designated field of practice. 2.1. Appropriate range and depth of learning in the technical domains comprising the field of practice informed by national and international benchmarks.
	Engineers Australia Curriculum Performance Indicators - 5.9. Skills in documenting results, analysing credibility of outcomes, critical reflection, developing robust conclusions, reporting outcomes.
	Engineers Australia Curriculum Performance Indicators - 3.1. An ability to communicate with the engineering team and the community at large. 3.4. An understanding of and commitment to ethical and professional responsibilities. 4.3. Proficiency in the engineering design of components, systems and/or processes in accordance with specified and agreed performance criteria. 5.5. Skills in the development and application of mathematical, physical and conceptual models, understanding of applicability and shortcomings.

Responding to student feedback

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

Updated the lecture content to include the state-of-the-art techniques in deep learning

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Alignment with Competency standards

Responding to student feedback

Responding to student feedback

Disclaimer

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