This module runs in Term 2 and is available for students on a course where it is a listed option (subject to restrictions*) and as an Unusual Option to students who have completed the prerequisite modules.
Pre-requisites:
Statistics Undergraduate students: ST218 Mathematical Statistics A, ST219 Mathematical Statistics B and ST221 Linear Statistical Modelling.*
MSc in Statistics students: ST903 Statistical Methods and ST952 Introduction to Statistical Practice.*
Master’s in Financial Mathematics students: MA907 Simulation and Machine Learning.
External Undergraduate students: ST220 Introduction to Mathematical Statistics and ST221 Linear Statistical Modelling.*
This module will introduce students to modern applications of Statistics in challenging modern data analysis contexts and provide them with the theoretical underpinnings to apply these methods.
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
Statistical Learning – an introduction to statistical learning theory, using simple ML methods to illustrate the various ideas:
From over-fitting to apparently complex methods which can work well, such as VC dimension and shattering sets.
PAC bounds. Loss functions. Risk (in the learning theoretic sense) and posterior expected risk. Generalisation error.
Supervised, unsupervised and semi-supervised learning.
The use of distinct training, test and validation sets, particularly in the context of prediction problems.
The Bootstrap revisited. Bags of Little Bootstraps. Bootstrap aggregation. Boosting.
Big Data and Big Model – issues and (partial) solutions:
The “curse of dimensionality”. Multiple testing; voodoo correlations, false-discovery rate and family-wise error rate. Corrections: Bonferroni, Benjamini-Hochberg.
Sparsity and Regularisation. Variable selection; regression. Spike and slab priors. Ridge Regression. The Lasso. The Dantzig Selector.
Concentration of measure and related inferential issues.
MCMC in high dimensions – preconditioned Crank Nicholson; MALA, HMC. Preconditioning. Rates of convergence.
By the end of the module, students should be able to:
View reading list on Talis Aspire
TBC
TBC
Type | Required | Optional |
---|---|---|
Lectures | 30 sessions of 1 hour (20%) | 2 sessions of 1 hour |
Private study | 90 hours (60%) | |
Assessment | 30 hours (20%) | |
Total | 150 hours |
Weekly revision of lecture notes and materials, wider reading, practice exercises and preparing for examination.
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Weighting | Study time | |
---|---|---|
Assignment 1 | 10% | 15 hours |
Due Term 2 Week 6. |
||
Assignment 2 | 10% | 15 hours |
Due Term 2 Week 9. |
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2 hour examination (April) | 80% | |
The examination paper will contain four questions, of which the best marks of THREE questions will be used to calculate your grade. ~Platforms - Moodle |
Weighting | Study time | |
---|---|---|
2 hour examination (September) | 100% | |
The examination paper will contain four questions, of which the best marks of THREE questions will be used to calculate your grade. ~Platforms - Moodle |
Solutions and cohort level feedback will be provided for the examination. Individual scripts are retained for external examiners and will not be returned.
This module is Optional for:
This module is Option list A for:
This module is Option list B for:
This module is Option list D for:
This module is Option list E for: