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 2024 in Semester 2 at Caulfield. It needs BFF5525 , BFF5370 or FIT9136.
- Credit points
- 6
- Offered in 2024
- Semester 2
- Caulfield
- Assessment
- No exam
- 1 task
- Workload
- 144 hours
- per semester
This is the 2024 handbook entry. See the 2027 entry.
Reviews
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Requisites
Before BFF5555
Prerequisites
Pass these before you enrol.
- BFF5525 Quantitative and data analysis in Python
- BFF5370FintechNo reviews yet
- FIT9136Algorithms and programming foundations in PythonNo reviews yet
After BFF5555
No unit lists BFF5555 as a prerequisite in the 2024 handbook.
Enrolment rules
You must be enrolled in B6001, B6002, B6003, B6004, B6005, B6011, B6036, B6038, B6039, B6043, B6048, B6051 to undertake this unit.
You must have passed BFF5525 or BFF5370 or FIT9136, or be granted approval by the Chief Examiner, to undertake this unit.
Overview
This unit introduces machine learning methods and applications in finance. It first provides an overview of machine learning as a statistical learning approach in contrast to traditional statistical analyses. The unit then covers principal categories of machine learning namely supervised learning, unsupervised learning and reinforcement learning. Techniques covered vary but may include K nearest neighbours, K mean clustering, principal component analysis and neural networks. Coming to the unit with a functional background in Python, you will learn to implement and evaluate these techniques in finance applications using Python.
Offerings in 2024
| Teaching period | Campus | Mode |
|---|---|---|
| Second semester | Caulfield | Flexible |
Assessment
- Within semester assessment100%
Learning outcomes
When you finish this unit, you should be able to:
- 1
have an advanced understanding of machine learning as a statistical learning approach in the area of artificial intelligence
- 2
describe principal categories and techniques in machine learning and the type of problems that can be addressed by each of these categories and techniques
- 3
implement and evaluate various machine learning techniques in Python in the context of a finance application
- 4
critically discuss issues and potential pitfalls in the implementation of machine learning techniques.
Workload and teaching
- Seminars24 hours
- Tutorials12 hours
- Teaching approachProblem-based learning
- Teaching approachActive 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 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.
This unit engages you in actively applying your knowledge, skills and attributes in interactive, collaborative and reflective activities.
Contacts
- Chief Examiners
- Dr Binh Do
Common questions
What are the prerequisites for BFF5555?
You need BFF5525 , BFF5370 or FIT9136 before you enrol. Enrolment rules also apply.
When is BFF5555 offered?
In 2024, 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 1 assessment task and no exam.