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  3. Machine Learning in Finance
  4. Eligibility and requirements

Machine Learning in Finance (FNCE30014) // Eligibility and requirements

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Eligibility and requirements

Prerequisites

One of

Code Name Teaching period Credit Points
FNCE10002 Principles of Finance
Semester 1 (On Campus - Parkville)
Summer Term (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
 12.5
FNCE20001 Business Finance No longer available

AND

One of

Code Name Teaching period Credit Points
ECOM20001 Econometrics 1
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
 12.5
ECON20003 Quantitative Methods 2
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
 12.5
ECON20005 Competition and Strategy
Semester 2 (On Campus - Parkville)
 12.5

Corequisites

None

Non-allowed subjects

Code Name Teaching period Credit Points
FNCE90084 Applied Machine Learning in Finance
Semester 2 (On Campus - Parkville)
 12.5
FNCE30012 Foundations of FinTech No longer available

Recommended background knowledge

Investments analysis; hands‐on experience with programming; exposure to computing (e.g. through taking COMP10001 in CIS).

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: 6 November 2025