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COMP5318: Machine Learning and Data Mining (2019 - Semester 2)

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Unit: COMP5318: Machine Learning and Data Mining (6 CP)
Mode: Normal-Evening
On Offer: Yes
Level: Postgraduate
Faculty/School: School of Computer Science
Unit Coordinator/s: Tran, Nguyen
Session options: Semester 1, Semester 2
Versions for this Unit:
Site(s) for this Unit:
Campus: Camperdown/Darlington
Pre-Requisites: None.
Brief Handbook Description: 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. The course requires that students have basic linear algebra and probability theory background and are competent at programming in a high level language (such as Python, Matlab or R).
Assumed Knowledge: INFO2110 OR ISYS2110 OR COMP9120 OR COMP5138.
Department Permission Department permission is required for enrollment in this session.
Lecturer/s: Tran, Nguyen
Timetable: COMP5318 Timetable
Time Commitment:
# Activity Name Hours per Week Sessions per Week Weeks per Semester
1 Lecture 2.00 1 13
2 Tutorial 1.00 1 13
3 Independent Study 9.00 13
T&L Activities: Tutorial: Tutorial

Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.

(6) Communication and Inquiry/ Research (Level 4)
1. Access relevant Data Mining research and develop interest in the field sufficient to take more advanced studies.
2. Present and interpret data and information in verbal and written form.
(2) Engineering/ IT Specialisation (Level 5)
3. Gain practical experience in using Machine Learning packages.
(3) Problem Solving and Inventiveness (Level 4)
4. Obtain practical experience in designing, implementing and evaluating Machine Learning algorithms.
(1) Maths/ Science Methods and Tools (Level 4)
5. Explain the basic principles and understand the strengths, weaknesses and applicability of Data Mining algorithms for solving classification, clustering and association tasks.
Assessment Methods:
# Name Group Weight Due Week Outcomes
1 Quiz 1 No 10.00 Week 4 5,
2 Quiz 2 (Mid-term) No 10.00 Week 9 5,
3 Assignment 1 Yes 15.00 Week 9 2, 3, 4, 5,
4 Assignment 2 Yes 15.00 Week 13 1, 2, 3, 5,
5 Final Exam No 50.00 Exam Period 5,
Assessment Description: Quiz 1: Background test (in the lab).

Quiz 2: Mid-term test.

Assignment: Ass.1: Classification task.

(It can be done in groups of 2 or 3).

Assignment: Ass.2: Method comparison. Due in week 13.

(It can be done in groups of 2 or 3).

Final Exam: Written Examination

The University has authorised and mandated the use of text-based similarity detecting software Turnitin for all text-based written assignments. This unit of study may also use MOSS for specialised detection of software code similiarity.

Assignments submitted electronically are to be consistently due at 23.59 on the submission day. For hard copy assignments/projects, you should naturally have a time during business hours.

Consistent penalty of 5% per day late, e.g.,

a) A “good” assignment that would normally get 9/10, and is 2 days late, loses 10% of the full 10 marks, ie new mark = 8/10.

b) An average assignment, that would normally get 5/10, that is 5 days late, loses 25% of the full 10 marks, ie new mark = 2.5/10

Assignments more than 10 days late get 0.

In some exceptional assessment task, no late assignments will be accepted (ie 1 second late = 0 marks).
Grading:
Grade Type Description
Standards Based Assessment Final grades in this unit are awarded at levels of HD for High Distinction, DI (previously D) for Distinction, CR for Credit, PS (previously P) for Pass and FA (previously F) for Fail as defined by University of Sydney Assessment Policy. Details of the Assessment Policy are available on the Policies website at http://sydney.edu.au/policies . Standards for grades in individual assessment tasks and the summative method for obtaining a final mark in the unit will be set out in a marking guide supplied by the unit coordinator.
Minimum Pass Requirement 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.
Policies & Procedures: 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.

Other policies

See the policies page of the faculty website at http://sydney.edu.au/engineering/student-policies/ for information regarding university policies and local provisions and procedures within the Faculty of Engineering and Information Technologies.
Prescribed Text/s: Note: Students are expected to have a personal copy of all books listed.
Recommended Reference/s: Note: References are provided for guidance purposes only. Students are advised to consult these books in the university library. Purchase is not required.
Online Course Content: USyd eLearning (webCT)

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Course Relations

The following is a list of courses which have added this Unit to their structure.

Course Year(s) Offered
Master of Data Science 2016, 2017, 2018, 2019, 2020
Bachelor of Advanced Computing/Bachelor of Commerce 2018, 2019, 2020
Bachelor of Advanced Computing/Bachelor of Science 2018, 2019, 2020
Bachelor of Advanced Computing/Bachelor of Science (Health) 2018, 2019, 2020
Bachelor of Advanced Computing/Bachelor of Science (Medical Science) 2018, 2019, 2020
Bachelor of Advanced Computing (Computational Data Science) 2018, 2019, 2020
Bachelor of Advanced Computing (Computer Science Major) 2018, 2019, 2020
Bachelor of Advanced Computing (Information Systems Major) 2018, 2019, 2020
Bachelor of Advanced Computing (Software Development) 2018, 2019, 2020
Bachelor of Computer Science and Technology (Honours) 2015, 2016, 2017
Bachelor of Computer Science and Technology (Honours) 2014 2013, 2014
Software Mid-Year 2016, 2017, 2018, 2019, 2020
Software/ Project Management 2019, 2020
Software 2015, 2016, 2017, 2018, 2019, 2020
Software / Arts 2016, 2017, 2018, 2019, 2020
Software / Commerce 2016, 2017, 2018, 2019, 2020
Software / Medical Science 2016, 2017
Software / Music Studies 2016, 2017
Software / Project Management 2016, 2017, 2018
Software / Science 2016, 2017, 2018, 2019, 2020
Software/Science (Health) 2018, 2019, 2020
Software / Law 2016, 2017, 2018, 2019, 2020
Software Engineering (till 2014) 2010, 2011, 2012, 2013, 2014
Software Engineering / Arts 2011, 2012, 2013, 2014
Software Engineering / Commerce 2010, 2011, 2012, 2013, 2014
Software Engineering / Medical Science 2011, 2012, 2013, 2014
Software Engineering / Project Management 2012, 2013, 2014
Software Engineering / Science 2011, 2012, 2013, 2014
Bachelor of Information Technology 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Arts 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Commerce 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Medical Science 2015, 2016, 2017
Bachelor of Information Technology/Bachelor of Science 2015, 2016, 2017
Bachelor of Information Technology (Computer Science) 2014 and earlier 2009, 2010, 2011, 2012, 2013, 2014
Information Technology (Computer Science)/Arts 2012, 2013, 2014
Information Technology (Computer Science) / Commerce 2012, 2013, 2014
Information Technology (Computer Science) / Medical Science 2012, 2013, 2014
Information Technology (Computer Science) / Science 2012, 2013, 2014
Information Technology (Computer Science) / Law 2012, 2013, 2014
Bachelor of Information Technology (Information Systems) 2014 and earlier 2010, 2011, 2012, 2013, 2014
Information Technology (Information Systems)/Arts 2012, 2013, 2014
Information Technology (Information Systems) / Commerce 2012, 2013, 2014
Information Technology (Information Systems) / Medical Science 2012, 2013, 2014
Information Technology (Information Systems) / Science 2012, 2013, 2014
Information Technology (Information Systems) / Law 2012, 2013, 2014
Bachelor of Information Technology/Bachelor of Laws 2015, 2016, 2017
Graduate Certificate in Information Technology 2015, 2016, 2017, 2018, 2019, 2020
Graduate Certificate in Information Technology Management 2015, 2016, 2017, 2018, 2019, 2020
Graduate Diploma in Computing 2015, 2016, 2017, 2018, 2019, 2020
Graduate Diploma in Health Technology Innovation 2016, 2017, 2018, 2019, 2020
Graduate Diploma in Information Technology 2015, 2016, 2017, 2018, 2019, 2020
Graduate Diploma in Information Technology Management 2015, 2016, 2017, 2018, 2019, 2020
Graduate Certificate in Information Technology (till 2014) 2012, 2013, 2014
Graduate Diploma in Complex Systems 2019, 2020
Graduate Diploma in Information Technology (till 2014) 2012, 2013, 2014
Master of Complex Systems 2019, 2020
Master of Health Technology Innovation 2016, 2017, 2018, 2019, 2020
Master of Information Technology 2015, 2016, 2017, 2018, 2019, 2020
Master of Information Technology Management 2015, 2016, 2017, 2018, 2019, 2020
Master of IT/Master of IT Management 2015, 2016, 2017, 2018, 2019, 2020
Master of Information Technology (till 2014) 2014
Software/Science (Medical Science Stream) 2018, 2019, 2020

Course Goals

This unit contributes to the achievement of the following course goals:

Attribute Practiced Assessed
(6) Communication and Inquiry/ Research (Level 4) No 9%
(7) Project and Team Skills (Level 3) No 0%
(8) Professional Effectiveness and Ethical Conduct (Level 3) No 0%
(5) Interdisciplinary, Inclusiveness, Influence (Level 4) No 0%
(4) Design (Level 4) No 0%
(2) Engineering/ IT Specialisation (Level 5) No 9.75%
(3) Problem Solving and Inventiveness (Level 4) No 3.75%
(1) Maths/ Science Methods and Tools (Level 4) No 77.5%

These goals are selected from Engineering & IT Graduate Outcomes Table 2018 which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table 2018 for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.