6 points, SCA Band 2, 0.125 EFTSL
Postgraduate - Unit
Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.
Faculty
Organisational Unit
School of Mathematical Sciences
Chief examiner(s)
Associate Professor Jonathan Keith
Coordinator(s)
Associate Professor Jonathan Keith
Unit guides
Co-requisites
Only students enrolled in the Master of Financial Mathematics can enrol in this unit. Exceptions can be made with permission from the unit co-ordinator.
Synopsis
Bayesian inference. Linear Gaussian models. Kalman filter. Maximum likelihood. Fischer information. Cramer-Rao bound. Supervised classification. Tree based methods. Support vector machines. Introduction to R.
Outcomes
On completion of this unit students will be able to:
- Develop specialised statistical knowledge and skills within the field of statistical learning.
- Understand the complex connections between specialised financial and mathematical concepts.
- Apply critical thinking to problems in statistical learning that relate to financial models.
- Apply estimation and calibration solving skills within the finance context.
- Formulate expert solutions to practical financial problems using specialised cognitive and technical skills within the fields of statistical learning.
- Communicate complex information in an accessible format to a non-mathematical audience.
Assessment
Examination (3 hours): 60% (Hurdle)
Continuous assessment: 40%
Hurdle requirement: To pass this unit a student must achieve at least 50% overall and at least 40% for the end-of-semester exam.
Workload requirements
Two 1.5-hour lectures and one 1-hour tutorial per week
See also Unit timetable information