MTH5540 · Statistical learning in finance

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

Assessment

The Handbook lists an examination for this unit.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

The Handbook lists no prerequisite, corequisite or prohibition for this unit.

Learning outcomes

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.

Workload

• Two 1.5 -hour seminars; • One 1-hour applied class (in weeks 2-12) and • Eight hours of independent study per week.

Student experience, not official rules. Nothing here changes what the Handbook says.