Notice
As part of Phase 3 of our return to campus plan, most units will now run tutorials, seminars and other small group activities on campus, and most will keep an online version available to those students unable to return or those who choose to continue their studies online.
To check the availability of face-to-face and online activities for your unit, please go to timetable viewer. To check detailed information on unit assessments visit your unit's iLearn space or consult your unit convenor.
Unit convenor and teaching staff |
Unit convenor and teaching staff
Unit Convenor
George Milunovich
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Credit points |
Credit points
10
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Prerequisites |
Prerequisites
BUSA8000
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Corequisites |
Corequisites
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Co-badged status |
Co-badged status
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Unit description |
Unit description
This unit introduces modern machine learning methodology which is used in solving many business problems in the modern world. Topics will be chosen from a wide set of possible areas including data analytics principles such as training and test data and the bias-variance tradeoff, modern approaches to regression including shrinkage techniques, tree based models and neural networks, methods for classification and the predictive analytics workflow. Emphasis throughout the unit will be on business applications drawn from a variety of fields. |
Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
On successful completion of this unit, you will be able to:
Assessment criteria for all assessment tasks will be provided on the unit iLearn site.
It is the responsibility of students to view their marks for each within-session-assessment on iLearn within 20 days of posting. If there are any discrepancies, students must contact the unit convenor immediately. Failure to do so will mean that queries received after the release of final results regarding assessment tasks (not including the final exam mark) will not be addressed.
Late submissions and extensions
Tasks 10% or less – No extensions will be granted. Students who have not submitted the task prior to the deadline will be awarded a mark of 0 for the task, except for cases in which an application for special consideration is made and approved.
Tasks above 10% - No extensions will be granted. There will be a deduction of 10% of the total available marks made from the total awarded mark for each 24 hour period or part thereof that the submission is late (for example, 25 hours late in submission – 20% penalty). This penalty does not apply for cases in which an application for special consideration is made and approved. No submission will be accepted after solutions have been posted.
Name | Weighting | Hurdle | Due |
---|---|---|---|
Programming tasks | 30% | No | Weeks 3, 5 and 9 (each worth 10%) |
Online Test | 30% | No | Week 6 |
Group Assignment | 40% | No | Week 13 |
Assessment Type 1: Practice-based task
Indicative Time on Task 2: 20 hours
Due: Weeks 3, 5 and 9 (each worth 10%)
Weighting: 30%
A sequence of tutorial assessments implementing computer code and performing related analytics tasks.
Assessment Type 1: Quiz/Test
Indicative Time on Task 2: 20 hours
Due: Week 6
Weighting: 30%
An open book online test will be held.
Assessment Type 1: Modelling task
Indicative Time on Task 2: 30 hours
Due: Week 13
Weighting: 40%
The group assignment is a hands-on project. Students will be required to develop a predictive model for a real-world dataset and implement it in computer script. Preliminary data analysis will be completed within a group (worth 20%). The follow-up analysis and write up will be completed individually (worth 20%).
1 If you need help with your assignment, please contact:
2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation
3 hours of teaching per week consisting of
Textbooks
Prescribed Textbook
The textbook for this unit is Python Machine Learning (Third Edition) by Sebastian Raschka and Vahid Mirjalili. Kindle and paperback versions are available from https://www.amazon.com.au/Python-Machine-Learning-Sebastian-Raschka/dp/1789955750/ . This book covers most but not all of the topics in the unit. The lecture and tutorial/computer lab notes will cover the additional material that you need to know. Further readings may be assigned for the various topics each week. This will either be journal articles, or other materials available on iLearn, web or available electronically via the Macquarie University Library.
Technology Used and Required
Required Unit Materials and/or Recommended Readings
Week | Topic | Textbook Chapter | Assessment |
---|---|---|---|
1 | Introduction | Ch. 1 | |
2 | Classification Algorithms - Part 1 | Ch. 2 | |
3 | Classification Algorithms - Part 2 | Ch. 3 | Programming Task 1 |
4 | Classification Algorithms - Part 3 | Ch. 3 | |
5 | Data Preprocessing | Ch. 4 | Programming Task 2 |
6 | ---- Class Test ---- | Class Test | |
Recess (2 weeks) | |||
7 | Dimensionality Reduction | Ch. 5 | |
8 | Model Evaluation and Hyperparameter Tuning | Ch. 6 | |
9 | Combining Different Models for Ensemble Learning | Ch. 7 | Programming Task 3 |
10 | Regression Analysis | Ch. 10 | |
11 | Clustering Analysis | Ch. 11 | |
12 | Applying Machine Learning to Sentiment Analysis | Ch. 8 | |
13 | Embedding a Machine Learning Model into a Web Application | Ch. 9 | Group Assignment |
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Unit information based on version 2021.03 of the Handbook