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Machine Learning & AI for Business (BUSA90542)
Graduate courseworkPoints: 12.5On Campus (Parkville)
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
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Contact information
Overview
Availability | May |
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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