2024/25 Taught Postgraduate Module Catalogue

OMAT5203M Statistical Learning

15 creditsClass Size: 100

Module manager: TBC
Email: .

Taught: 1 Mar to 30 Apr, 1 Mar to 30 Apr (2mth)(adv yr) View Timetable

Year running 2024/25

Pre-requisite qualifications

N/A

Pre-requisites

OMAT5101M

Statistical Methods

Module replaces

N/A

This module is not approved as an Elective

Module summary

Statistical learning is at the core of the modern world. Online advertising, automated vehicles, stock market trading, transport planning all use statistical models to learn from past data and make decisions about the future. Statistical learning is a way to rigorously identify patterns in data and to make quantitative predictions. It is how we translate data into knowledge.

Objectives

This introduces key concepts and techniques in statistical learning which are relevant to a number of practical applications. These techniques include statistical machine learning for classification and regression.

Learning outcomes
On completion of this module students should be able to:

1. Explain the classification and regression problem;
2. Assess the error of a fitted model and explain the fitting algorithm;
3. Understand the statistical foundations of different classification and regression methods;
4. Understand the importance of uncertainty and evaluate the uncertainty in simple model predictions; and
5. Perform classification and regression tasks using real-world data.

Skills outcomes
Skills developed in this module include:

- Self direction and effective decision making in complex and unpredictable situations.
- Interpreting data and making decisions based on that interpretation.- Communicating the outcome of data analysis.

Syllabus

Indicative content for this module includes:

- Introduction to classification and regression
- Statistical decision theory, loss functions
- Optimisation, gradient descent, local & global optima
- Regression methods
- Tree models
- Ensemble methods e.g. Boosting, Random forests

Teaching methods

Delivery type	Number	Length hours	Student hours
On-line Learning	1	1.50	1.50
On-line Learning	5	1.00	5.00
Discussion forum	6	2.00	12.00
Independent online learning hours			42.00
Private study hours			89.50
Total Contact hours			18.50
Total hours (100hr per 10 credits)			150.00

Opportunities for Formative Feedback

Online learning materials will provide regular opportunity for students to check their understanding (for example through formative MCQs with automated feedback). Regular group activity embedded into learning will allow opportunities for formative feedback from peers and tutors.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Online Assessment	MCQ test and short answer questions	20.00
Assignment	Project Report	80.00
Total percentage (Assessment Coursework)		100.00

Students will resit by completing the Assignment (which covers all learning outcomes) at the next running of the module.

Reading list

There is no reading list for this module

Last updated: 29/04/2024 16:18:46

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