OCMP5329: Semester 2a, 2023

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Units OCMP5329 Semester 2a 2023 [Online]

Unit of study_

OCMP5329: Deep Learning

Semester 2a, 2023 [Online] - Online Program

Overview

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

Unit code	OCMP5329
Academic unit	Computer Science
Credit points	6
Prohibitions ?	COMP5329 or COMP4329
Prerequisites ?	None
Corequisites ?	None
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:
Assignment	Coding Assignment Coding Assignment	40%	Week 04 Due date: 25 Aug 2023 at 23:59	4 Weeks
Outcomes assessed:
Assignment	Final Project (Progress Check) Final Project (Progress Check)	6%	Week 06 Due date: 08 Sep 2023 at 23:59	3 Weeks
Outcomes assessed:
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:

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 sydney.edu.au/students/guide-to-grades.

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.

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.

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

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.

Outcomes
Graduate qualities

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
Learning outcomes

Responding to student feedback

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.

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

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

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