Statistical Machine Learning (COMP90051) // Eligibility and requirements

You’re viewing the 2022 Handbook:
Or view archived Handbooks
You’re currently viewing the 2022 version of this subject
Subjects taught in 2022 will be in one of three delivery modes: Dual-Delivery, Online or On Campus.
From 2023 most subjects will be taught on campus only with flexible options limited to a select number of postgraduate programs and individual subjects.
To learn more, visit COVID-19 course and subject delivery.

Eligibility and requirements

Prerequisites

Students must meet one of the following prerequisite options:

Option 1

One of

Code Name Teaching period Credit Points
COMP90049 Introduction to Machine Learning
Semester 2 (Dual-Delivery - Parkville)
Semester 1 (Dual-Delivery - Parkville)
 12.5
COMP30027 Machine Learning
Semester 1 (Dual-Delivery - Parkville)
 12.5
COMP30018 Knowledge Technologies No longer available

Option 2

Admission into the MC-DATASC Master of Data Science

AND

One of

Code Name Teaching period Credit Points
COMP20008 Elements of Data Processing
Semester 2 (Dual-Delivery - Parkville)
Semester 1 (Dual-Delivery - Parkville)
 12.5
MAST90105 Methods of Mathematical Statistics
Semester 1 (Dual-Delivery - Parkville)
 25

Option 3

Admission into or selection of one of the following:

  • 100pt Program course entry point in the MC-IT Master of Information Technology
  • 150pt Program course entry point in the MC-IT Master of Information Technology
  • Distributed Computing specialisation (formal) in the MC-IT Master of Information Technology
  • Computing specialisation (formal) in the MC-IT Master of Information Technology
  • Artificial Intelligence specialisation (formal) in the MC-IT Master of Information Technology

Option 4

Admission into the 100pt Program course entry point in the MC-IT Master of Information Technology

AND

Selection of the Cyber Security specialisation (formal) in the MC-IT Master of Information Technology

Option 5

Admission into the MC-DATASC Master of Data Science

AND

Selection of the Data Science Background Stream

Corequisites

None

Non-allowed subjects

433-484 Machine Learning

433-679 Evolutionary and Neural Computation

433-680 Machine Learning

433-684 Machine Learning

Recommended background knowledge

Basic probability

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