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Machine Learning & AI for Business (BUSA90542)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

Availability
May
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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
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

DescriptionTimingPercentage

5 x individual in-class quizzes

  • 200 words (each)
During the teaching period25%

Syndicate assignment and presentation

  • 1000 words (equivalent)
Week 525%

Final Examination

  • 2.5 hours
Hurdle requirement: Students are required to pass the final examination to pass the subject		Week 950%

Last updated: 31 January 2024

Dates & times

  • May
    Mode of deliveryOn Campus (Parkville)
    Contact hours
    Total time commitment170 hours
    Teaching period27 May 2024 to 19 July 2024
    Last self-enrol date 6 June 2024
    Census date14 June 2024
    Last date to withdraw without fail 5 July 2024
    Assessment period ends26 July 2024

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