Program Overview

Statistics with Machine Learning MSc

Overview

The Statistics with Machine Learning MSc course is designed to provide students with advanced knowledge and skills in statistical machine learning, enabling them to understand and build methods used in everyday life. The course differs from the Statistics MSc by providing a particular focus on statistical machine learning.

Course Structure

The course is split between core and optional modules. Students will study a total of 180 credits, consisting of 120 credits worth of taught modules and a 60-credit dissertation.

Core Modules

• Statistical Foundations: This module introduces the fundamental principles and techniques underlying modern statistical and data analysis.
• Classical and Bayesian Inference: This module explores the two main theories of statistical inference, namely classical (frequentist) inference and Bayesian inference.
• Statistical Machine Learning: This module builds on principles of statistical inference and linear regression, introducing a variety of methods of regression and classification.
• Statistics Dissertation: Students will work on a substantial investigation on a topic in statistics or probability, in collaboration with staff from the school's internationally recognised Statistics and Probability Section or industry partners.

Optional Modules

• Applied Multivariate Statistics: This module explores the analysis of multivariate data, including principal components analysis, modelling and inference for multivariate data, and classification of observation vectors into subpopulations.
• Computational Statistics: This module explores how computers allow the easy implementation of standard, but computationally intensive, statistical methods and innovative numerical methods.
• Machine Learning and Inference for Differential Equations: This module considers modern machine learning tools within a framework for building and training surrogates for computer models derived from Differential Equations.
• Time Series and Forecasting: This module provides a general introduction to the analysis of data that arise sequentially in time, including several commonly occurring models and their properties.
• Statistical Modelling: This module extends the theoretical aspects of statistical inference by developing the theory of the generalised linear model and its practical implementation.

Entry Requirements

• Undergraduate degree: A high 2:2 (55% or international equivalent) Honours degree in mathematics or a closely related subject with substantial mathematical content.
• Portfolio: Applicants should have a solid background in mathematics, including calculus, linear algebra, probability, and statistics at degree level.
• English language requirements: IELTS 6.0 (no less than 5.5 in each element).

Fees

• Home / UK: £13,450
• International: £27,150

Careers

• Careers advice: The university offers individual careers support for all postgraduate students.
• Job prospects: Statisticians are required to work in many sectors, including education, finance, healthcare, sport, and transport.
• Graduate destinations: Previous graduates work as analysis officers, business analysts, and pricing model analysts in organisations such as Capital One, HMRC, and the Lowell Group.

Related Courses

• Statistics MSc
• Statistical Science (Distance Learning) MSc
• Business Analytics MSc
• Economics MSc
• Machine Learning in Science MSc