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
S1-FF-CLAYTON-FLEXIBLE

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 summary

Examination (3 hours and 10 minutes): 60% (Hurdle)

Continuous assessment: 40%

Hurdle requirement: If you would otherwise have passed the unit but do not achieve at least 45% of the marks available for the end-of-semester examination you will receive a Hurdle Fail (NH) grade and a mark of 45 on your transcript.

Workload requirements

Workload

Other unit costs

Costs are indicative and subject to change.
Miscellaneous items required (Printing, Stationery)- $100.