1. Handbook
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  3. Machine Learning
  4. Eligibility and requirements

Machine Learning (COMP30027) // Eligibility and requirements

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Eligibility and requirements

Prerequisites

Prerequisites

All of

Code Name Teaching period Credit Points
COMP10002 Foundations of Algorithms
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
 12.5
COMP20008 Elements of Data Processing
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
 12.5

OR

Admission into the MC-SOFTENG Master of Software Engineering

Corequisites

None

Non-allowed subjects

Code Name Teaching period Credit Points
COMP90049 Introduction to Machine Learning
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
 12.5
ACTL30008 Actuarial Analytics and Data I
Semester 1 (On Campus - Parkville)
 12.5

Recommended background knowledge

Basic probability theory, equivalent to material covered in Victorian Certificate of Education (VCE) Mathematical Methods 3/4.

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: 8 January 2025