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.
- First semester 2019 (On-campus)
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.
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.
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.
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.
Two 1.5-hour lectures and one 1-hour applied class per week
See also Unit timetable information