Skip to main content
Unit outline_

OCMP5329: Deep Learning

Semester 2a, 2023 [Online] - Online Program

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.

Unit details and rules

Academic unit Computer Science
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
COMP5329 or COMP4329
Assumed knowledge
? 

OCMP5318 or COMP5318 or COMP4318

Available to study abroad and exchange students

No

Teaching staff

Coordinator Nataliia Stratiienko, nataliia.stratiienko@sydney.edu.au
Type Description Weight Due Length
Assignment Final Project (Project Abstract)
Final Project (Project Abstract)
6% Week 03
Due date: 18 Aug 2023 at 23:59
3 Weeks
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
Assignment Coding Assignment
Coding Assignment
40% Week 04
Due date: 25 Aug 2023 at 23:59
4 Weeks
Outcomes assessed: LO1 LO4 LO3 LO2
Assignment Final Project (Progress Check)
Final Project (Progress Check)
6% Week 06
Due date: 08 Sep 2023 at 23:59
3 Weeks
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
Assignment Final Project (Final Report and Codes)
Final Project (Final Report and Codes)
48% Week 08
Due date: 22 Sep 2023 at 23:59
2 Weeks
Outcomes assessed: LO1 LO2 LO3 LO4 LO5

Assessment summary

Coding Assignment – writing a computer program to solve a given task and a report discussing the results.
Final Project – writing a computer program to solve a given task and a report discussing the results.

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.

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:

Coding Assignment and Final Project - late submissions are allowed up to 10 calendar days late. A penalty of 5% per day late will apply.

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.

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
Multiple weeks Coding Assignment Independent study (10 hr) LO1 LO2 LO4
Final Project Independent study (32 hr) LO1 LO2 LO3 LO4 LO5
Week 01 Multilayer Neural Network (Videos) Online class (1 hr) LO1 LO2 LO3
Multilayer Neural Network (Further Readings) Independent study (3 hr) LO3 LO5
Multilayer Neural Network (Coding) Independent study (1 hr) LO4
Multilayer Neural Network (Live Session) Tutorial (1.5 hr) LO1 LO2 LO3 LO4 LO5
Week 02 Optimization and Regularization (Videos) Online class (1 hr) LO2
Optimization and Regularization (Further Readings) Independent study (3 hr) LO2 LO5
Optimization and Regularization (Coding) Independent study (1 hr) LO4
Optimization and Regularization (Live Session) Tutorial (1.5 hr) LO2 LO4 LO5
Week 03 Convolutional Neural Networks (Videos) Online class (1 hr) LO1 LO3
Convolutional Neural Networks (Further Readings) Independent study (3 hr) LO3 LO5
Convolutional Neural Networks (Coding) Independent study (1 hr) LO4
Convolutional Neural Networks (Live Session) Tutorial (1.5 hr) LO1 LO3 LO4 LO5
Week 04 Recurrent Neural Networks (Videos) Online class (1 hr) LO1 LO3
Recurrent Neural Networks (Further Readings) Independent study (3 hr) LO3 LO5
Recurrent Neural Networks (Coding) Independent study (1 hr) LO4
Recurrent Neural Networks (Live Session) Tutorial (1.5 hr) LO1 LO3 LO4 LO5
Week 05 Transformer Neural Networks (Videos) Online class (1 hr) LO1 LO3
Transformer Neural Networks (Further Readings) Independent study (3 hr) LO3 LO5
Transformer Neural Networks (Coding) Independent study (1 hr) LO4
Transformer Neural Networks (Live Session) Tutorial (1.5 hr) LO1 LO3 LO4 LO5
Week 06 Graph Convolutional Networks (Videos) Online class (1 hr) LO1 LO3
Graph Convolutional Networks (Further Readings) Independent study (3 hr) LO3 LO5
Graph Convolutional Networks (Coding) Independent study (1 hr) LO4
Graph Convolutional Networks (Live Session) Tutorial (1.5 hr) LO1 LO3 LO4 LO5
Week 07 Self-Study Week Independent study (10 hr) LO1 LO2 LO3 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.

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. Students will be able to demonstrate and build deep neural networks using fundamental layers and modules
  • LO2. Students will be able to explain the principles and insights behind optimization and regularization techniques of deep neural networks
  • LO3. Students will be able to compare different deep learning architectures, including MLP, CNN, RNN, Transformer, and GNN, as well as their characteristics
  • LO4. Students will be able to implement and train various deep learning models with codes
  • LO5. Student will get familiar with application scenarios and recent research of deep learning

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

This is the first time this unit is being offered.

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.