MATH6112 Computer-based statistical modelling
Module Overview
The aim of the course is to provide a modern view of computer-based data analysis, from the statistical point of view. The course is intended for students with a solid basic background in probability, statistical methods, and computing, and who aim to build on this background. Topics are covered at a brisk pace; to make the best of this course, students can expect to put in significant self-study.
Aims and Objectives
Module Aims
The overarching aim of the course is to provide the student with modern view of computer-based data analysis, from the statistical point of view. The course gives a unified and comprehensive approach to the subject.
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- - Formulate statistical models and estimate their parameters. Interpret the estimated model.
- - Produce a technical report that describes problem formulation and solution
- Construct models and predictions that can be expected to have good statistical properties, based on exploring alternative models and using cross-validation.
Syllabus
- Revision of probability essentials - Introduce maximum-likelihood estimation and develop it for selected parametric models such as independent sampling and regression, including generalized linear models. Develop confidence intervals and hypothesis tests via the asymptotic normality result - Introduce optimal statistical decisions. Define a loss function and develop optimal binary classification as the main example. - Selected topics in model selection. In-sample and out-of-sample error. Cross-validation for model selection.
Learning and Teaching
Teaching and learning methods
A range of teaching and learning methods is used, centered on lectures, computer laboratories, and private study.
| Type | Hours |
|---|---|
| Teaching | 16 |
| Independent Study | 59 |
| Total study time | 75 |
Resources & Reading list
Hastie, T and R. Tibshirani and J. Friedman (2009). The elements of statistical learning.
Assessment
Summative
| Method | Percentage contribution |
|---|---|
| Coursework assignment(s) | 100% |
Repeat
| Method | Percentage contribution |
|---|---|
| Coursework assignment(s) | 100% |
Referral
| Method | Percentage contribution |
|---|---|
| Coursework assignment(s) | 100% |
Repeat Information
Repeat type: Internal & External
Linked modules
Pre-requisites: - MATH1024 and MATH2010 or equivalent maturity with Probability and Statistics - Basic familiarity with programming in matlab or R or equivalent.