BFF5555 Financial machine learning
Faculty of Business and Economics
BFF5555 Financial machine learning is a level 5, 6-credit-point, postgraduate unit from the Faculty of Business and Economics, offered in 2026 in Semester 2 at Caulfield. It needs BFF5370, BFF5255, BFF5525 or FIT9136.
- Credit points
- 6
- Offered in 2026
- Semester 2
- Caulfield
- Assessment
- No exam
- 3 tasks
- Workload
- 144 hours
- per semester
This is the 2026 handbook entry. See the 2027 entry.
Reviews
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Requisites
Before BFF5555
Prerequisites
Pass these before you enrol.
- BFF5370FintechNo reviews yet
- BFF5255Investment analytics in PythonNo reviews yet
- BFF5525 Quantitative and data analysis in Python
- FIT9136Introduction to Python programmingNo reviews yet
After BFF5555
No unit lists BFF5555 as a prerequisite in the 2026 handbook.
Enrolment rules
You must be enrolled in B6001, B6002, B6003, B6004, B6005, B6011, B6036, B6038, B6039, B6043, B6048, B6051 or S6001 to undertake this unit.
You must have passed BFF5525 or BFF5370 or FIT9136 or BFF5255, or be granted approval by the Chief Examiner, to undertake this unit.
Overview
This unit explores the application of machine learning techniques in financial contexts, emphasising practical implementation using Python. Beginning with foundational concepts in statistical learning, the unit progresses through key supervised learning methods such as linear regression, classification, and tree-based models, before introducing unsupervised learning and deep learning techniques. Special attention is given to model evaluation, regularisation, and cross-validation. The unit culminates in advanced topics including natural language processing (NLP) in finance. Through interactive seminars and hands-on assessments, you will develop the skills to build, assess, and apply machine learning models to real-world financial problems.
Offerings in 2026
| Teaching period | Campus | Mode |
|---|---|---|
| Second semester | Caulfield | Flexible |
Assessment
- Project40%
- Written40%
- Quiz / Test20%
Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.
Learning outcomes
When you finish this unit, you should be able to:
- 1
explain machine learning as a statistical learning paradigm and distinguish it from traditional statistical approaches
- 2
identify and describe key machine learning categories (supervised, unsupervised) and techniques, including their financial applications
- 3
implement machine learning models in Python, including regression, classification, tree-based methods, and deep learning, tailored to finance scenarios
- 4
evaluate model performance using techniques such as cross-validation and regularisation, and discuss challenges like overfitting
Workload and teaching
- Seminars36 hours
- Teaching approachActive learning
- Teaching approachProblem-based learning
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. You are expected to complete all pre-class activities prior to your scheduled class, and post-class activities should be completed after your scheduled class. Learning activities may include a combination of teacher directed, peer directed and online engagement activities.
This unit engages you in actively applying your knowledge, skills and attributes in interactive, collaborative and reflective activities.
This unit includes problem-based learning approaches, where you engage in research, integrate theory and practice and apply knowledge and skills to develop viable solutions in response to a problem or set of problems.
Learning resources
Technology resources
There may be an additional cost associated with purchasing a physical and/or virtual calculator. Specific details will be provided in the Learning Management System by commencement of Orientation week.
Contacts
- Chief Examiners
- Dr Hoa Briscoe-Tran
Common questions
What are the prerequisites for BFF5555?
You need BFF5370, BFF5255, BFF5525 or FIT9136 before you enrol. Enrolment rules also apply.
When is BFF5555 offered?
In 2026, BFF5555 runs in Semester 2 at Caulfield.
How much work is BFF5555?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does BFF5555 have an exam?
No. BFF5555 has 3 assessment tasks and no exam.