ELEC5307: Semester 2, 2022

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Units ELEC5307 Semester 2 2022 [Normal day]

Unit of study_

ELEC5307: Advanced Signal Processing with Deep Learning

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.

Details

Academic unit	Electrical and Information Engineering
Unit code	ELEC5307
Unit name	Advanced Signal Processing with Deep Learning
Session, year ?	Semester 2, 2022
Attendance mode	Normal day
Location	Remote
Credit points	6

Enrolment rules

Prohibitions ?	None
Prerequisites ?	None
Corequisites ?	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 and contact details

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 ?

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 sydney.edu.au/students/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.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

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.

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.

Closing the loop

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

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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

Details

Enrolment rules

Teaching staff and contact details

Assessment

Assessment criteria

Late submission

Special consideration

Academic integrity

Weekly schedule

Study commitment

Required readings

Learning outcomes

Graduate qualities

Outcome map

Alignment with Competency standards

Closing the loop

Disclaimer

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