COMP5318: Semester 2, 2020

University home

Library

Current students

Staff intranet

Find an event

Give

Current students

Units COMP5318 Semester 2 2020 [Normal evening]

Unit of study_

COMP5318: Machine Learning and Data Mining

Semester 2, 2020 [Normal evening] - Camperdown/Darlington, Sydney

Overview

Machine learning is the process of automatically building mathematical models that explain and generalise datasets. It integrates elements of statistics and algorithm development into the same discipline. Data mining is a discipline within knowledge discovery that seeks to facilitate the exploration and analysis of large quantities for data, by automatic and semiautomatic means. This subject provides a practical and technical introduction to machine learning and data mining. Topics to be covered include problems of discovering patterns in the data, classification, regression, feature extraction and data visualisation. Also covered are analysis, comparison and usage of various types of machine learning techniques and statistical techniques.

Unit details and rules

Unit code	COMP5318
Academic unit	Computer Science
Credit points	6
Prohibitions ?	None
Prerequisites ?	None
Corequisites ?	None
Assumed knowledge ?	Programming skills
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Nguyen Tran, nguyen.tran@sydney.edu.au
Lecturer(s)	Nguyen Tran, nguyen.tran@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Open book)	Final exam Online exam. 40% barrier on exam (less than 40% in the exam is a FAILED)	50%	Formal exam period	2 hours
Outcomes assessed:
Small test	Mid-term quiz Mid-term quiz.	15%	Week 06	1 hour
Outcomes assessed:
Assignment	Assingnment 1 Computer program	15%	Week 08	n/a
Outcomes assessed:
Assignment	Assignment 2 Computer program and report; individual or in groups of 2 students.	20%	Week 12	n/a
Outcomes assessed:
= hurdle task ? = group assignment ? = Type C final exam ?

Assessment summary

Assignment 1 – writing a computer program to solve a given task.
Assignment 2 – writing a computer program to solve a given task and a report discussing the results.
Mid-term quiz - small online quiz test to review mid-term contents.
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	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.

Also, there’s a 40% barrier on the final exam (less than 40% in the exam is automatically a FAIL).

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:

Assignment 1 and Assignment 2 - late submissions are allowed up to 3 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
Week 01	1. Administrative matters and course overview. Introduction to machine learning and data mining.	Online class (3 hr)
Week 02	Linear Algebra Review, SVD, and PCA.	Online class (3 hr)
Week 03	Probability Review	Online class (3 hr)
Week 04	KNN, Naïve Bayes, ROC	Online class (3 hr)
Week 05	Linear Regression, Overfitting	Online class (3 hr)
Week 06	Logistic Regression, Mid-term Quiz	Online class (3 hr)
Week 07	Support Vector Machines	Online class (3 hr)
Week 08	Muti-class Classification	Online class (3 hr)
Week 09	Clustering	Online class (3 hr)
Week 10	Perceptron, Neural Networks	Online class (3 hr)
Week 11	Introduction to Deep Learning	Online class (3 hr)
Week 12	Review	Online class (3 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.

Required readings

Textbooks:

1. Kevin P. Murphy (2012), Machine Learning: A Probabilistic Perspective.

2. Jure Leskovec, Anand Rajaraman, Jeff llman (2014), Mining of Massive Datasets.

Books for the practical part using Python:

1. Andreas C. Mueller and Sarah Guido (2016). Introduction to Machine Learning with Python: a Guide for Data Scientists. O'Reilly.

2. Aurelien Geron (2019). Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow. O'Reilly.

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. understand the basic principles, strengths, weaknesses and applicability of machine learning algorithms for solving embedding, classification, regression, clustering.
LO2. have obtained practical experience in designing, implementing and evaluating machine learning algorithms
LO3. have gained practical experience in using machine learning software and libraries
LO4. present and interpret data and information in verbal and written form

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.

Redeveloped course - new slides, tutorials and assessments; new topics. New teaching team.

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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect