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Unit of study_

COMP5329: Deep Learning

This course provides an introduction to deep machine learning, which is rapidly emerging as one of the most successful and widely applicable set of techniques across a range of applications. Students taking this course will be exposed to cutting-edge research in machine learning, starting from theories, models, and algorithms, to implementation and recent progress of deep learning. Specific topics include: classical architectures of deep neural network, optimization techniques for training deep neural networks, theoretical understanding of deep learning, and diverse applications of deep learning in computer vision.


Academic unit Computer Science
Unit code COMP5329
Unit name Deep Learning
Session, year
Semester 1, 2021
Attendance mode Normal evening
Location Remote
Credit points 6

Enrolment rules

Assumed knowledge


Available to study abroad and exchange students


Teaching staff and contact details

Coordinator Chang Xu,
Type Description Weight Due Length
Final exam (Record+) Type B final exam Final exam
Exam on Canvas
60% Formal exam period 2 hours
Outcomes assessed: LO1 LO3 LO4 LO5 LO6
Assignment group assignment Assignment 1
take-home assignment.
20% Week 07 n/a
Outcomes assessed: LO1 LO3 LO5 LO6 LO7
Assignment group assignment Assignment 2
take-home assignment.
20% Week 12 n/a
Outcomes assessed: LO2 LO7 LO6 LO4 LO3
group assignment = group assignment ?
Type B final exam = Type B final exam ?

Assignment 1 – writing a computer program to solve a given task and a report discussing the results.
Assignment 2 – writing a computer program to solve a given task and a report discussing the results.
Exam – online exam at the end of the semester (less than 40% is automatically a FAIL)
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

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Assignment 1 and Assignment 2 - late submissions are allowed up to 3 days late. A penalty of 5% per day late will apply.

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 honesty, academic dishonesty, 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 dishonesty or plagiarism seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic dishonesty. If such matches indicate evidence of plagiarism or other forms of dishonesty, your teacher is required to report your work for further investigation.

WK Topic Learning activity Learning outcomes
Week 01 Introduction Online class (3 hr) LO1
Week 02 Multilayer neural networks Online class (3 hr) LO2 LO3 LO4
Week 03 Optimization for Deep Models Online class (3 hr) LO2 LO3 LO4
Week 04 Regularization for Deep Models Online class (3 hr) LO2 LO3 LO4
Week 06 Convolutional Neural Networks Online class (3 hr) LO2 LO3 LO4
Week 07 Neural Network Architectures Online class (3 hr) LO2 LO3 LO4
Week 08 Recurrent Neural Networks Online class (3 hr) LO2 LO3 LO4
Week 09 Transformer Neural Networks Online class (3 hr) LO2 LO3 LO4
Week 10 Graph Convolutional Networks Online class (3 hr) LO2 LO3 LO4
Week 11 Deep Learning Applications Online class (3 hr) LO1 LO5 LO6
Week 12 Deep Generation Models Online class (3 hr) LO2 LO3 LO4
Week 13 Review Online class (3 hr) LO1 LO3 LO7

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.

Goodfellow I J, Bengio Y, Courville A, 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. demonstrate knowledge of the broad range of deep learning applications, such as image classification, object detection, image segmentation and face recognition
  • LO2. use deep learning software to create deep learning prototypes
  • LO3. evaluate deep learning algorithms
  • LO4. demonstrate knowledge of the main methods of deep neural network design and evaluation and the relative strengths and weaknesses of each, and their most appropriate uses
  • LO5. model application problems as deep learning problems
  • LO6. apply and tailor known deep learning algorithms for solving new challenging problems
  • LO7. present the design and evaluation of a deep learning prototype, defining the requirements, describing the design processes and evaluation.

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
- Balance the introduction on theoretical foundations and practical uses of deep learning.


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