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Statistical Data Modelling (MATH3702)
We study statistical models, including regression and the general and generalised linear models. We estimate model parameters in the classical and Bayesian inference frameworks, using R and Stan software. We describe related computer techniques, including computational matrix algebra and Markov chain Monte Carlo algorithms. We work with multiple data sources using state-of-the art data handling tools.
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Financial Statistics (MATH3703)
This module introduces students to financial time series analysis and modelling, illustrated using a variety of applications from the finance industry. We study univariate and multivariate time series models, as well as inferential techniques. Model selection, forecasting and 'curse of dimensionality' problems are treated from both a methodological and a computational point of view.
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Medical Statistics (MATH3710)
This module equips students with the skills to plan and analyse clinical trials, including crossover and sequential designs, and to perform sample size calculations. The principles of meta-analysis are introduced. Epidemiology is studied, including case-control and cohort studies. Survival analysis is covered in detail. Students gain experience with computer packages that are used in health and medicine.
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Machine Learning (COMP3003)
This module introduces machine learning, covering unsupervised, supervised and reinforcement learning from a Bayesian perspective. This includes theory behind a range of learning techniques and how to apply these representations of data in systems that make decisions and predictions.
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Big Data Analytics (COMP3008)
The key objective of this module is to familiarise the students with the most important information technologies used in manipulating, storing and analysing big data. Students will work with semi-structured datasets and choose appropriate storage structures for them. A representative of recent non-relational trends is presented—namely, graph-oriented databases.
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Partial Differential Equations (MATH3701)
This module deepens students’ understanding of partial differential equations and applies them to real world problems. It provides a variety of analytic and numerical methods for their solution. It includes a wide range of applications such as transport, heat diffusion, wave propagation and nonlinear phenomena.
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Industrial Placement (MATH3706)
This module provides an opportunity for final year students to gain experience of applying mathematics in a professional environment. Students can carry out a placement in a wide variety of areas, including data science, finance, management, research, and software development. As part of this, they develop a range of skills that considerably increase future employment opportunities.
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Relativity and Cosmology (MATH3707)
This module introduces the basic concepts of special and general relativity, such as the Lorentz transformations, time dilation, and the curvature of space-time. These ideas help students to understand the basic concepts of modern cosmology, including the standard model of the expanding universe (FLRW model) and its extensions using dark matter and dark energy.
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Modelling and Numerical Simulation (MATH3708)
Simulations and modelling are crucial tools that support industrial research and innovation. Students will learn to analyse mathematical models and develop programs to solve them. They will investigate algorithms and discuss their performance. Students will code and run numerical programs on a high performance computer. These forward-looking skills are highly sought after by many employers.
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Optimisation, Networks and Graphs (MATH3709)
Optimisation and graph theory are related branches of mathematics with applications in areas as diverse as computer science and logistics. Graphs are used to capture relationships between objects, while optimisation studies algorithms that search for optimal solutions. This module provides both the theory and modern algorithms, including those used in artificial intelligence, required to solve a broad range of problems.
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Mathematics of Planet Earth (MATH3712)
Students work in small groups to research problems directly related to sustainability and the protection of the environment, so addressing some of the most serious problems faced by humanity. This can involve the solution of mathematical, statistical, computational, industrial or economic problems, or challenges in renewable energy engineering. Students present their conclusions orally and in a professional report.
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Project (MATH3713)
In this module, students perform individual independent research into a topic in Mathematical Sciences, or Mathematics Education. Students choose a subject to explore in depth, which they are particularly interested in, and receive regular advice and feedback from an expert supervisor. The outputs of the project are a dissertation and a presentation. This module is an ideal preparation for progressing to further study.
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School Placement (MATH3714)
This module provides an opportunity for final year students to gain experience in teaching and to develop their key educational skills by working in a school environment for one morning a week over both semesters. Students typically progress from assisting in the classroom to teaching a starter activity over the academic year.
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Geometry and Algebra (MATH3604)
A review of group theory leads into an exploration of plane affine, hyperbolic, and projective geometries, all from the Kleinian point of view. Then an introduction to rings and fields is given with applications in geometry emphasised. These topics are key ideas in the study of pure mathematics.