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Unit outline_

ELEC5307: Advanced Signal Processing with Deep Learning

Semester 2, 2022 [Normal day] - Camperdown/Darlington, Sydney

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 Electrical and Information Engineering
Credit points 6
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


Teaching staff

Coordinator Luping Zhou,
Lecturer(s) Luping Zhou,
Type Description Weight Due Length
Final exam (Open book) Type C final exam Final exam
Open-book exam for two hours
60% Formal exam period 2 hours
Outcomes assessed: LO2 LO3
Assignment Project 1
Group work
20% Week 08
Due date: 25 Sep 2022 at 23:59
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment Project 2
Group work
20% Week 11 n/a
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
Type C final exam = 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


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



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 to deep learning (e.g., historical review of machine learning and deep learning, basic machine learning concepts, performance evaluation) Lecture (2 hr) LO2
Week 02 Lecture: Basics of Neural Networks; Tutorial 1: Python Basics Lecture and tutorial (3 hr) LO2
Week 03 Lecture: CNN Architectures; Tutorial 2: Python Packages Lecture and tutorial (3 hr) LO1 LO2 LO3
Week 04 Lecture: CNN Applications (I); Tutorial 3: DataLoader in PyTorch Lecture and tutorial (3 hr) LO1 LO2 LO3
Week 05 Lecture: Practical in Neural Network; Tutorial 4: Forward Process in PyTorch Lecture and tutorial (3 hr) LO1 LO2 LO3
Week 06 Lecture: Graphical Model and RNN; Tutorial 5: Backward Process in PyTorch Lecture and tutorial (3 hr) LO1 LO2 LO3
Week 07 Lecture: Deep Generative Models; Project 1: Parameters in Neural Networks Lecture and tutorial (3 hr) LO1 LO2 LO3
Week 08 Lecture: Invited Guest Lecture; Project 1: Parameters in Neural Networks (continue) Lecture and tutorial (3 hr) LO1 LO2 LO3
Week 09 Lecture: Applications (II); Project 2: Classification Challenge (data release and start) Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4
Week 10 Lecture: Attention and Transformer; Project 2: Classification Challenge (continue) Lecture and tutorial (3 hr) LO1 LO2 LO3
Week 11 Lecture: Learning with Few Labels; Project 2: Classification Challenge (continue) and due Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 12 Lecture: Invited Guest Lectures; Project 2: Classification Challenge Presentation Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 13 Review Lecture and tutorial (3 hr) 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.

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 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

Alignment with Competency standards

Outcomes Competency standards
Engineers Australia Curriculum Performance Indicators - EAPI
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 - EAPI
5.9. Skills in documenting results, analysing credibility of outcomes, critical reflection, developing robust conclusions, reporting outcomes.
Engineers Australia Curriculum Performance Indicators - EAPI
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.

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


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.