Unit convenor and teaching staff |
Unit convenor and teaching staff
George Milunovich
|
---|---|
Credit points |
Credit points
10
|
Prerequisites |
Prerequisites
BUSA7000
|
Corequisites |
Corequisites
|
Co-badged status |
Co-badged status
|
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:
Late submissions of assessments
Unless a Special Consideration request has been submitted and approved, no extensions will be granted. There will be a deduction of 10% of the total available assessment-task marks made from the total awarded mark for each 24-hour period or part thereof that the submission is late. Late submissions will only be accepted up to 96 hours after the due date and time.
No late submissions will be accepted for timed assessments – e.g., quizzes, online tests.
Table 1: Penalty calculation based on submission time
Submission time after the due date (including weekends) |
Penalty (% of available assessment task mark) |
Example: for a non-timed assessment task marked out of 30 |
< 24 hours |
10% |
10% x 30 marks = 3-mark deduction |
24-48 hours |
20% |
20% x 30 marks = 6-mark deduction |
48-72 hours |
30% |
30% x 30 marks = 9-mark deduction |
72-96 hours |
40% |
40% x 30 marks = 12-mark deduction |
> 96 hours |
100% |
Assignment won’t be accepted |
Special Consideration
To request an extension on the due date/time for a timed or non-timed assessment task, you must submit a Special Consideration application. An application for Special Consideration does not guarantee approval.
The approved extension date for a student becomes the new due date for that student. The late submission penalties above then apply as of the new due date.
Name | Weighting | Hurdle | Due |
---|---|---|---|
Programming tasks | 30% | No | Week 3, 5 and 9 |
Class Test | 30% | No | Week 6 |
Individual Assignment | 40% | No | Week 13 |
Assessment Type 1: Practice-based task
Indicative Time on Task 2: 20 hours
Due: Week 3, 5 and 9
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%
A test of approximately 60 minutes duration to be held in the session.
Assessment Type 1: Modelling task
Indicative Time on Task 2: 30 hours
Due: Week 13
Weighting: 40%
The assignment is a hands-on project. Students will be required to understand and clean a complex real-world dataset, develop a predictive model for it and implement their work in Python script.
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
Prescribed Textbook
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 | |
7 | Dimensionality Reduction | Ch. 5 | |
Recess (2 weeks) | |||
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 | Individual assignment |
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Macquarie University offers a range of Student Support Services including:
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Unit information based on version 2022.05 of the Handbook