Module manager: Dr Seppo Virtanen
Email: S.Virtanen@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2024/25
MATH5741M, or equivalent.
This module is not approved as an Elective
Statistical learning is at the core of the modern world. Online advertising, automated vehicles, stock market trading, transport planning: each uses 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. In this module the fundamental concepts of statistical learning are introduced and the student will learn to use several key statistical models widely employed in science and industry.
To introduce basic techniques from statistical machine learning for classification and regression.
On completion of the module a student should:
(a) Be able to explain the classification and regression problem;
(b) Be able to assess the error of a fitted model and explain the fitting algorithm;
(c) Understand the statistical foundations of different classification and regression methods;
(d) Understand the importance of uncertainty and evaluate the uncertainty in simple model predictions;
(e) Be able to perform classification and regression tasks using existing software packages;
(f) Be able to carry out and justify a simple statistical model analysis of real world data.
- Introduction to classification and regression
- Statistical decision theory, loss functions
- Optimisation, gradient descent, local & global optima
- Linear regression
- Logistic regression
- Tree models
- Ensemble methods: e.g. Boosting, Random forests
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 22 | 1 | 22 |
| Practical | 11 | 1 | 11 |
| Private study hours | 117 | ||
| Total Contact hours | 33 | ||
| Total hours (100hr per 10 credits) | 150 | ||
The student will be expected to complete regular written worksheet assignments testing their understanding of theoretical course elements.
The student will learn to perform linear and logistic regression using the software package R. The student will also learn to fit tree models using the software package R. Part of the assessment for the module consists of a practical, where the student will apply these techniques to a real-world data set.
Monitoring by regular worksheets and achievement in supervised practical sessions.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Practical | 3 assessed practical worksheets | 50 |
| Total percentage (Assessment Coursework) | 50 | |
There is no resit available for the coursework components of this module. If the module is failed, the coursework mark will be carried forward and added to the resit exam mark with the same weighting as listed above.
| Exam type | Exam duration | % of formal assessment |
|---|---|---|
| Standard exam (closed essays, MCQs etc) | 2.0 Hrs 0 Mins | 50 |
| Total percentage (Assessment Exams) | 50 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
The reading list is available from the Library website
Last updated: 4/29/2024
Errors, omissions, failed links etc should be notified to the Catalogue Team