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
MSc students:
Non-statistics students.
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
Evaluate, select and apply appropriate mathematical and/or probabilist techniques.
Demonstrate knowledge of and facility with formal probability concepts, both explicitly and by applying them to the solution of problems.
Create structured and coherent arguments communicating them in written form.
Construct logical mathematical arguments with clear identification of assumptions and conclusions.
Reason critically, carefully, and logically and derive (prove) mathematical results.
Problem solving: Use rational and logical reasoning to deduce appropriate and well-reasoned conclusions. Retain an open mind, optimistic of finding solutions, thinking laterally and creatively to look beyond the obvious. Know how to learn from failure.
Self awareness: Reflect on learning, seeking feedback on and evaluating personal practices, strengths and opportunities for personal growth.
Communication: Present arguments, knowledge and ideas, in a range of formats.
Professionalism: Prepared to operate autonomously. Aware of how to be efficient and resilient. Manage priorities and time. Self-motivated, setting and achieving goals, prioritising tasks.
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.
Students can register for this module without taking any assessment.
Weighting | Study time | |
---|---|---|
Assignment 1 | 10% | 15 hours |
The assignment will contain a number of questions for which solutions and / or written responses will be required. |
||
Assignment 2 | 10% | 15 hours |
The deadline for the assignment can be found in the Statistics Assessment Handbook (http://warwick.ac.uk/STassessmenthandbook). |
||
In-person Examination | 80% | |
The examination paper will contain four questions, of which the best marks of THREE questions will be used to calculate your grade.
|
Weighting | Study time | |
---|---|---|
In-person Examination - Resit | 100% | |
The examination paper will contain four questions, of which the best marks of THREE questions will be used to calculate your grade.
|
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: