Handbook home
Machine Learning & AI for Business (BUSA90542)
Graduate courseworkPoints: 12.5On Campus (Parkville)
To learn more, visit 2023 Course and subject delivery.
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Overview
Availability | May |
---|---|
Fees | Look up fees |
This component builds on the material in Statistical Learning for Business and covers advanced analytic methods. It extends the statistical learning component of Business Analytics Foundations in three ways. First, new techniques such as tree based methods and neural networks are introduced. Second, students will be introduced to unsupervised statistical learning techniques and third, students will learn how to combine models and techniques to produce ensembles with better predictive capabilities.
Intended learning outcomes
On completion of this subject, students should be able to:
- Demonstrate how to quantitatively analyse large datasets and convert raw data into relevant information for management decisions, using a wide variety of parametric and non-parametric techniques.
- Understand the difference between supervised and unsupervised statistical learning.
- Determine which techniques to apply to different types of data.
- Understand how to perform model averaging.
Last updated: 31 January 2024
Eligibility and requirements
Prerequisites
Pre-requisite
Code | Name | Teaching period | Credit Points |
---|---|---|---|
BUSA90537 | Coding for Business Problems |
July (On Campus - Parkville)
March (On Campus - Parkville)
|
12.5 |
Corequisites
None
Non-allowed subjects
None
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 31 January 2024
Assessment
Description | Timing | Percentage |
---|---|---|
5 x individual in-class quizzes
| During the teaching period | 25% |
Syndicate assignment and presentation
| Week 5 | 25% |
Final Examination
| Week 9 | 50% |
Last updated: 31 January 2024
Dates & times
- May
Mode of delivery On Campus (Parkville) Contact hours Total time commitment 170 hours Teaching period 29 May 2023 to 21 July 2023 Last self-enrol date 8 June 2023 Census date 16 June 2023 Last date to withdraw without fail 7 July 2023 Assessment period ends 28 July 2023
What do these dates mean
Visit this webpage to find out about these key dates, including how they impact on:
- Your tuition fees, academic transcript and statements.
- And for Commonwealth Supported students, your:
- Student Learning Entitlement. This applies to all students enrolled in a Commonwealth Supported Place (CSP).
Subjects withdrawn after the census date (including up to the ‘last day to withdraw without fail’) count toward the Student Learning Entitlement.
Last updated: 31 January 2024
Further information
- Texts
Prescribed texts
There are no specifically prescribed or recommended texts for this subject.
Last updated: 31 January 2024