2024/25 Taught Postgraduate Module Catalogue

OGDS5204M Statistical Learning

15 creditsClass Size: 150

Module manager: TBC
Email: .

Taught: 1 Jul to 31 Aug View Timetable

Year running 2024/25

Pre-requisite qualifications

Students are required to meet the programme entry requirements prior to studying the module.

This module is mutually exclusive with

OMAT5203M

Statistical Learning

Module replaces

None

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
Discussion forum	6	2.00	12.00
WEBINAR	1	1.50	1.50
WEBINAR	5	1.00	5.00
Independent online learning hours			42.00
Private study hours			89.50
Total Contact hours			18.50
Total hours (100hr per 10 credits)			150.00

Private study

Across each week of learning students will actively engage with pre-prepared teaching and learning resources which scaffold learners to achieve learning outcomes (independent online learning). Each week follows a set pattern of acquiring knowledge which is then applied to a substantive activity which will usually be authentic to real-world application. Weekly asynchronous discussions (such as discussion boards) allow for peer-to-peer and peer-to-tutor discussion which supports completion of the substantive activity. At the end of each week of learning students consolidate their learning through reflective activities and a weekly live webinar session with the module tutor. Each unit also provides students with the opportunity for exploration and self-directed learning as is expected at masters level (private study).

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 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: 18/11/2024

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