The University of Southampton
Courses

# MATH6112 Computer Analysis of Data and Models

## 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:

• - 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.
• - Formulate statistical models and estimate their parameters. Interpret the estimated model.

### 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.

TypeHours
Teaching16
Independent Study59
Total study time75

Hastie, T and R. Tibshirani and J. Friedman (2009). The elements of statistical learning.

### Assessment

#### Summative

MethodPercentage contribution
Coursework assignment(s) 100%

#### Referral

MethodPercentage contribution
Coursework assignment(s) 100%

#### Repeat Information

Repeat type: Internal & External

Pre-requisites: - MATH1024 and MATH2010 or equivalent maturity with Probability and Statistics - Basic familiarity with programming in matlab or R or equivalent.

### Costs

#### Costs associated with this module

Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study.

In addition to this, students registered for this module typically also have to pay for:

##### Books and Stationery equipment

There are no additional compulsory costs associated with the module

Please also ensure you read the section on additional costs in the University’s Fees, Charges and Expenses Regulations in the University Calendar available at www.calendar.soton.ac.uk.