COMP5328: Semester 2, 2020

Current students

Units COMP5328 Semester 2 2020 [Normal evening]

During 2021 we will continue to support students who need to study remotely due to the ongoing impacts of COVID-19 and travel restrictions. Make sure you check the location code when selecting a unit outline or choosing your units of study in Sydney Student. Find out more about what these codes mean. Both remote and on-campus locations have the same learning activities and assessments, however teaching staff may vary. More information about face-to-face teaching and assessment arrangements for each unit will be provided on Canvas.

Unit of study_

COMP5328: Advanced Machine Learning

Overview

Machine learning models explain and generalise data. This course introduces some fundamental machine learning concepts, learning problems and algorithms to provide understanding and simple answers to many questions arising from data explanation and generalisation. For example, why do different machine learning models work? How to further improve them? How to adapt them to different purposes? The fundamental concepts, learning problems and algorithms are carefully selected. Many of them are closely related to practical questions of the day, such as transfer learning, learning with label noise and multi-view learning.

Details

Academic unit	Computer Science
Unit code	COMP5328
Unit name	Advanced Machine Learning
Session, year ?	Semester 2, 2020
Attendance mode	Normal evening
Location	Camperdown/Darlington, Sydney
Credit points	6

Enrolment rules

Prohibitions ?	None
Prerequisites ?	None
Corequisites ?	COMP5318 OR COMP3308 OR COMP3608
Available to study abroad and exchange students	No

Teaching staff and contact details

Coordinator	Tongliang Liu, tongliang.liu@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Take-home short release)	Final exam Written examination	50%	Formal exam period	3 hours (including reading time)
Final exam (Take-home short release)	Outcomes assessed:
Assignment	Assignment 1 Written report	25%	Week 08	4 weeks
Assignment	Outcomes assessed:
Assignment	Assignment 2 Written report	25%	Week 12	4 weeks
Assignment	Outcomes assessed:
= hurdle task ? = group assignment ? = Type D final exam ?

Assignments: Demonstrating the knowledge and skills from a given problem description.

Final exam: The final exam covers all aspects of the course.

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. It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.

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 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.

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to Machine Learning Problems	Lecture (2 hr)
Week 01	Introduction to Machine Learning Problems	Tutorial (1 hr)
Week 02	Loss Functions and Convex Optimisation	Lecture (2 hr)
Week 02	Loss Functions and Convex Optimisation	Tutorial (1 hr)
Week 03	Hypothesis Complexity and Generalisation	Lecture (2 hr)
Week 03	Hypothesis Complexity and Generalisation	Tutorial (1 hr)
Week 04	Dictionary Learning and NMF	Lecture (2 hr)
Week 05	Sparse Coding and Regularisation	Lecture (2 hr)
Week 05	Dictionary Learning and NMF	Tutorial (1 hr)
Week 06	Learning with Noisy Data	Lecture (2 hr)
Week 06	Sparse Coding and Regularisation	Tutorial (1 hr)
Week 07	Domain Adaptation and Transfer Learning	Lecture (2 hr)
Week 07	Learning with Noisy Data	Tutorial (1 hr)
Week 08	Learning with Noisy Data II: Label Noise	Lecture (2 hr)
Week 08	Domain Adaptation and Transfer Learning	Tutorial (1 hr)
Week 09	Reinforcement Learning	Lecture (2 hr)
Week 09	Learning with Noisy Data II: Label Noise	Tutorial (1 hr)
Week 10	Causal Inference	Lecture (2 hr)
Week 10	Reinforcement Learning	Tutorial (1 hr)
Week 11	Multi-task Learning	Lecture (2 hr)
Week 11	Causal Inference	Tutorial (1 hr)
Week 12	Review	Lecture (2 hr)
Week 12	Multi-task Learning	Tutorial (1 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. Present the design and evaluation of a machine learning algorithm, describing the design processes and evaluation
LO2. Understand the variance and bias trade-off in machine learning algorithms
LO3. Understand and analyse some machine learning algorithms and have some knowledge to further improve them
LO4. Understand and analyse some machine learning problems and have some knowledge to adapt the existing machine learning models to different purposes
LO5. Implement machine learning algorithms from peer-reviewed papers
LO6. Understand the nature of the statistical foundations of designing or adapting learning algorithms
LO7. At the completion of this unit, you should be able to demonstrate knowledge of the introduced machine learning models and the relative strengths and weaknesses of each and their most appropriate uses
LO8. At the completion of this unit, you should be able to demonstrate knowledge of methods to analyse machine learning algorithms, such as hypothesis complexities and generalisation bounds.

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

Closing the loop

No significant changes have been made since this unit was last offered

Additional information

IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism.

In assessing a piece of submitted work, the School of Computer Science may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so.

Computer programming assignments may be checked by specialist code similarity detection software. The Faculty of Engineering currently uses the MOSS similarity detection engine (see http://theory.stanford.edu/~aiken/moss/), or the similarity report available in ED (edstem.org). These programs work in a similar way to TurnItIn in that they check for similarity against a database of previously submitted assignments and code available on the internet, but they have added functionality to detect cases of similarity of holistic code structure in cases such as global search and replace of variable names, reordering of lines, changing of comment lines, and the use of white space.

All written assignments submitted in this unit of study will be submitted to the similarity detecting software program known as Turnitin. Turnitin searches for matches between text in your written assessment task and text sourced from the Internet, published works and assignments that have previously been submitted to Turnitin for analysis.

There will always be some degree of text-matching when using Turnitin. Text-matching may occur in use of direct quotations, technical terms and phrases, or the listing of bibliographic material. This does not mean you will automatically be accused of academic dishonesty or plagiarism, although Turnitin reports may be used as evidence in academic dishonesty and plagiarism decision-making processes.

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

General laboratory safety rules

No eating or drinking is allowed in any laboratory under any circumstances
A laboratory coat and closed-toe shoes are mandatory
Follow safety instructions in your manual and posted in laboratories
In case of fire, follow instructions posted outside the laboratory door
First aid kits, eye wash and fire extinguishers are located in or immediately outside each laboratory
As a precautionary measure, it is recommended that you have a current tetanus immunisation. This can be obtained from University Health Service: unihealth.usyd.edu.au/

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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.

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