DTSC71-302: Statistical Learning and Regression Models

Description

This subject covers the theory and practice of modern statistical learning, regression and classification modelling. Techniques covered range from traditional model selection and generalised linear model structures to modern, computer-intensive methods including generalised additive models, splines and tree methods. Methods to handle continuous, ordinal and nominal response variables and assessment of fit via cross-validation and residual diagnostics are also considered.  All techniques will be investigated via practical application on real data using the statistical software package R.

Subject details

Learning outcomes

1. Demonstrate advanced knowledge of the limitations of linear regression models and the ability to develop an appropriate regression model.
2. Evaluate and choose between a variety of regression models.
3. Demonstrate advanced knowledge of the role of regularisation and the ability to use the concept to develop a variety of regression models.
4. Apply regression models for limited dependent variables (i.e., binomial, ordered and count data).
5. Apply generalised linear models, including proper use and assessment of model diagnostic techniques.
6. Develop regression models utilising splines, additive models and tree-based methods.
7. Correctly and concisely communicate the results and implications of a regression analysis in a professional written report.

Enrolment requirements

Subject outlines

• May 2021 [Standard - ‎]
• May 2020 [Standard - ‎]

Subject dates