Statistical Machine Learning (Distance Learning)
| Code | School | Level | Credits | Semesters |
| MATH4079 | Mathematical Sciences | 4 | 20 | Autumn UK |
- Code
- MATH4079
- School
- Mathematical Sciences
- Level
- 4
- Credits
- 20
- Semesters
- Autumn UK
Summary
Statistical Machine Learning is a topic at the interface between statistics and computer science that concerns models that can adapt to and make predictions based on data. The module builds on principles of statistical inference and linear regression developed in Foundations of Statistics and Statistical Inference to introduce a variety of methods of clustering, dimension reduction, regression and classification. Much of the focus is on the bias-variance trade-off, and on methods to measure and compensate for overfitting. The learning approach is hands-on, with students using R extensively in studying contemporary statistical machine learning methods, and in applying them to tackle challenging real-world applications.
Target Students
Available to MSc Statistics Science (Distance Learning) students.
Classes
- One 1-hour tutorial each week for 10 weeks
- One 1-hour computing each week for 10 weeks
This module is designed for distance learning programmes where delivery of material will largely be asynchronous through course notes support by lecture videos, Moodle quizzes and exercises. The tutorial will be used to support and reinforce the asynchronous learning. The computer software learning will be through specially designed workbooks with support sessions through online computer labs.
Assessment
- 30% Project 1: Demonstrate ability to review, appraise and apply statistical machine learning methods approaches and interpret analysis. Individual project comprising a written report.
- 50% Project 2: Demonstrate ability to review, appraise and apply statistical machine learning methods approaches to an open-ended problem and interpret analysis. Individual project comprising a written report.
- 10% Presentation 1: Individual oral presentation on open-ended project. To assess oral communication of methodology, modelling and analysis.
- 10% Presentation 2: Individual oral presentation on project 1. To assess oral communication of methodology, modelling and analysis.
Assessed by end of autumn semester
Educational Aims
The course provides students with skills that employers highly value in statistical graduates, including general skills such as problem solving and proficient oral and written communications; and the specific technical skills needed to develop and apply machine learning approaches for challenging real world applications.Learning Outcomes
A student who completes this course successfully will be able to:
L1 - state and apply standard results relating to methods of supervised learning;
L2 - explain the suitability and limitations of various machine learning methods in given applications;
L3 - use statistical software packages to apply methods to data sets, make predictions and assess predictive accuracy;
L4 - evaluate and implement a strategy for approaching machine learning problems that are open ended and of non-routine nature;
L5 - synthesise and present a systematic account of machine learning concepts applied to given problems, communicating results using appropriate style and terminology.