Overview
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.
Offerings
S1-01-CLAYTON-ON-CAMPUS
Rules
Enrolment Rule
Contacts
Chief Examiner(s)
Associate Professor Jonathan Keith
Unit Coordinator(s)
Associate Professor Jonathan Keith
Learning outcomes
On successful completion of this unit, you should be able to:
1.
Develop specialised statistical knowledge and skills within the field of statistical learning.
2.
Understand the complex connections between specialised financial and mathematical concepts.
3.
Apply critical thinking to problems in statistical learning that relate to financial models.
4.
Apply estimation and calibration solving skills within the finance context.
5.
Formulate expert solutions to practical financial problems using specialised cognitive and technical skills within the fields of statistical learning.
6.
Communicate complex information in an accessible format to a non-mathematical audience.
Assessment
1 - Continuous assessment
2 - Final assessment - Exam (3 hours and 10 minutes)
Scheduled and non-scheduled teaching activities
Applied sessions
Seminars
Workload requirements
Workload
Other unit costs
Costs are indicative and subject to change.
Miscellaneous items required (printing, stationery) - $100