MA Public Opinion and Political Behaviour
BSc Statistics options

Final Year, Component 06

MA831-6-FY or (MA829-6-AU plus one option from list)
Nonlinear Programming

How do you apply an algorithm or numerical method to a problem? What are the advantages? And the limitations? Understand the theory and application of nonlinear programming. Learn the principles of good modelling and know how to design algorithms and numerical methods. Critically assess issues regarding computational algorithms.

Combinatorial Optimisation

In this module you will learn what underpins the algorithms used where variables are integer and apply these algorithms to solve integer and mixed integer problems with cutting-plane algorithms.

Bayesian Computational Statistics

What do you understand about Bayes’ theorem and Bayesian statistical modelling? Or about Markov chain Monte Carlo simulation? Focus on Bayesian and computational statistics. Understand the statistical modelling and methods available. Learn to develop a Monte Carlo simulation algorithm for simple probability distributions.

Artificial intelligence and machine learning with applications

Artificial Intelligence is the science of making computers and machines to produce results and behave in a way that resembles human intelligence. This multidisciplinary activity involves the knowledge of different disciplines such as computer science, Mathematics and statistics, but also includes important elements from philosophy, logic and even psychology. Nowadays, AI is well embedded in our society from self-driving cars to spam filters, and from finance trading to video games. All predictions state that more and more of our society will depend on this technology with the consequent transformation of our society and economy. The impact of AI affects any discipline and therefore it is important for everyone to understand its principles, applications and limitations. This module is suitable for any student regardless of their background. This module will provide you with a broad overview of AI, as well as more detailed understanding of core concepts and models. We will follow an approach both theoretical and practical, describing the theory and fundamentals of machine learning models, as well as showing how to implement them and their applications.

Dynamic programming and reinforcement learning
Capstone Project: Mathematics
Capstone Project: Mathematics

This is a two-term project for which a student should undertake about 150 hours work. Students will gain experience of some branch of mathematics, statistics, operational research or the interface of these disciplines with other fields. The student should also gain experience of solo work involving research concerning some previously unknown topic, the production of a project report and an oral examination.

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