Notice
As part of Phase 3 of our return to campus plan, most units will now run tutorials, seminars and other small group learning activities on campus for the second half-year, while keeping an online version available for those students unable to return or those who choose to continue their studies online.
To check the availability of face to face 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
George Milunovich
Angela Chow
|
---|---|
Credit points |
Credit points
10
|
Prerequisites |
Prerequisites
BUSA8000
|
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:
Assessment Marks
It is the responsibility of students to view their marks for each within session assessment on iLearn within 20 working 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 marks (not including the final exam mark) will not be addressed.
Extensions and Penalties on Within Session Assessment Tasks
Name | Weighting | Hurdle | Due |
---|---|---|---|
Online Test | 30% | No | Week 7 |
Programming tasks | 30% | No | Weeks 3, 5, 9 |
Group Assignment | 40% | No | Week 13 |
Assessment Type 1: Quiz/Test
Indicative Time on Task 2: 20 hours
Due: Week 7
Weighting: 30%
An open book online test will be held.
Assessment Type 1: Practice-based task
Indicative Time on Task 2: 20 hours
Due: Weeks 3, 5, 9
Weighting: 30%
A sequence of tutorial assessments implementing computer code and performing related analytics tasks.
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
Classes
Textbooks
Technology Used and Required
Required Unit Materials and/or Recommended Readings
Week | Lecture Topic/Book Chapter | Computer Lab/Tutorial | Assessment |
1 | Introduction / Ch 1 | Yes | |
2 | Classification Algorithms - Part 1 / Ch 2 | Yes | |
3 | Classification Algorithms - Part 2 / Ch 3 | Yes | Programming Task 1 (10%) |
4 | Classification Algorithms - Part 3 / Ch 3 | Yes | |
5 | Building Good Training Sets - Data Preprocessing / Ch 4 | Yes | Programming Task 2 (10%) |
6 | Dimensionality Reduction / Ch 5 | Yes | |
7 | Class Test | Yes | Class test (30%) |
Recess | |||
8 | Model Evaluation & Hyperparameter Tuning / Ch 6 | Yes | |
9 | Regression Analysis - Part 1 / Ch 10 | Yes | Programming Task 3 (10%) |
10 | Regression Analysis - Part 2 / Ch 10 | Yes | |
11 | Combining Different Models - Ensemble Learning / Ch 7 | Yes | |
12 | Text Analysis / Ch 8 | Yes | |
13 | Clustering Analysis / Ch 11 | Yes | Group Assignment (40%) |
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