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.
School of Mathematical Sciences
- First semester 2017 (Day)
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.
Weekly homework: 10% + Assignments: 10% + Minor project: 10% + Examination: 70%
Two 1.5-hour lectures and one 1-hour tutorial per week
See also Unit timetable information
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.