Study options
- Starting in
- September 2025
- Location
- Mile End
- Fees
- Home: £15,250
Overseas: £29,950
EU/EEA/Swiss students
What you'll study
This MSc will teach you the fundamental concepts of Artificial Intelligence with a focus on the theoretical knowledge and practical skills required to apply machine learning techniques to a variety of scientific industries, research areas and real-world problems.
Our core compulsory modules explore probability, statistics, machine learning and deep learning, providing you with a solid foundation in AI. You will also gain experience with leading tools, software, and programming languages such as R, Python, C++ and SQL, among others.
You will also work closely with a Queen Mary researcher on a research project in which you can apply your AI and Machine Learning skills to a high-profile dataset or active research being conducted in areas such as computational chemistry, epidemiology, renewable energy and many more.
Structure
3 compulsory modules
4 elective modules
Research project in Data Science
Compulsory/Core modules
This module establishes the probability and statistics background required for students applying techniques or doing other advanced statistics Modules. The Module begins by covering the essential theoretical notions of probability and the distributions of random variables which underpin statistical methods. It then describes different types of statistical tests of hypotheses and addresses the questions of how to use them and when to use them. This material is essential for applications of statistics in psychology, life or physical sciences, business or economics.
The module covers fundamental concepts of machine learning with emphasis on the development of practical skills required for the selection and application of machine learning methods to defined problems. Topics include data representation and preparation, unsupervised learning methods, regression and classification methods, artificial neural networks and performance evaluation. Face-to-face teaching will be combined with extensive hands-on sessions in the computational lab.
The students work on research topics in one of the areas of Artificial Intelligence and Machine Learning in Science set by their project supervisors. Computational work is the principal component of the projects. The work also involves critical evaluation of previously published results. A dissertation is prepared.
The research methods module is designed to help you attain the relevant skills to assess, understand, and visualise data and to undertake your research project. This includes essential skills such as communication and organising information from the literature, through to being able to extract information on data science methods from a multidisciplinary environment and report writing. A strong emphasis will be placed on enabling you to engage with complex information from seminars and to discuss that information to explore how it relates to material studied on your programme. Discussion sessions will be a key part in helping you develop as a data scientist and enhance transferable skills that will benefit you in the rest of your degree and future employment.
Elective modules
This module introduces you to the Python programming language. After learning about data types, variables and expressions, you will explore the most important features of the core language including conditional branching, loops, functions, classes and objects. We will also look at several of the key packages (libraries) that are widely used for numerical programming and data analysis.
This module introduces a selection of numerical methods for solving applied mathematical problems. One of the most fascinating aspects of physical theories is the fact that highly complex behaviour can arise from the repeated application of simple rules - consider for example the motion of bodies under gravity, which combine to give us the rich structure of our Universe. Such systems are the focus of much current research, and whilst they are too complex for an analytic approach, their numerical solution is (at least in principle) straightforward. The aim of the course is to introduce students to a selection of computational algorithms that are used to solve problems in applied mathematics, and to provide them with practical skills in software development that will be useful in many fields both within and outside of academic work. The module uses python as the main coding language. Some experience with python or similar coding languages will be an advantage but will not be assumed.
The module aims to introduce you to the Bayesian paradigm. The module will show some of the problems with frequentist statistical methods and demonstrate that the Bayesian paradigm provides a unified approach to problems of statistical inference and prediction. In the Module you will learn to make Bayesian inferences in a variety of problems, and apply Bayesian methods in real-life examples.
This module will present methods for time series analysis.These will allow the student to understand better how to use and extract information from historical business data series. In particular, the student will learn how to extract the pivotal concepts of time series data, including the trend and cyclic components of a data series, calculate the autocorrelation, learn about autoregressive and moving average models, and cointegration.The module will develop the notions around realistic business examples and an implementation of the methods will be provided using the statistic software R..
The ML in Materials Discovery module is designed to help you understand how artificial intelligence and machine learning can be applied to the domain of materials science for materials discovery and help you attain a deeper understanding of ML methods applied to real scientific datasets to refine your practical skills. In this module you will learn the basics of modern chemical informatics, and how AI and ML methods can be exploited to study material properties. Then you will apply these computational methods to design new materials, and to model and predict their properties. You will have the opportunity to apply these techniques to specific cutting-edge examples.
The Cloud computing in AI module is designed to familiarise yourself with the latest Cloud computing and decentralised applications technologies in the context of data management and AI and ML applications. This module will allow you to build working knowledge of the fundamentals of data management and data processing and then to explore network concepts, types of devices and data center functions. You will learn about services provided on the top 'Big Clouds' and practice on how to combine these services to support AI analyses and modelling. You will acquire confidence in applying all the tools learned in your master programme to the widest range of computing and business environments.
The AI in Astrophysics and Space Science module is designed to help you understand how artificial intelligence and machine learning can be applied to the astrophysics and space-science domains and help you attain a deeper understanding of ML methods applied to real scientific datasets to refine your practical skills and to help prepare for your independent study research project irrespective of the specific problem domain of that. In this module you will learn about data preparation and pathologies related to the use of artificial intelligence and machine learning, you will apply the methods you have studied in your other AI and ML and your Deep Learning modules, and you will explore knowledge-guided machine learning.
This module addresses one of the most important 'hot topics' in mathematics research - the study of networks - and is essential for understanding the characteristics and universal structural properties of complex networks. Complex networks are the outcome usually of a stochastic dynamics but they are not completely random. You will learn how to disentangle randomness from structural organisational principles of complex networks and how several major types of complex network can be described and artificially generated by mathematical models. Networks characterise the underlying structure of a large variety of complex systems, from the Internet to social networks and the brain. This course is designed to teach students the mathematical language needed to describe complex networks, their basic properties and dynamics. The broad aim is to provide students with the key skills required fundamental research in complex networks, and necessary for application of network theory to specific network problems arising in academic or industrial environments.
Assessment
- 67%% Modules
- 33%% Research project
- Taught modules are assessed through a combination of coursework and written examinations.
- You will also be assessed through an individual research project.
Research project
Supervised research projects will be available in a variety of scientific fields. These will be offered in-line with the research expertise of the School of Physical and Chemical Sciences and the School of Mathematical Sciences.
A list of potential projects will be added in the coming weeks.
Teaching
Modules are taught through a combination of lectures, seminars and computer lab sessions. Most modules last one semester only, but Machine and Deep Learning will be taught across both semesters.
You’ll be assigned an Academic Adviser who will guide you in both academic and pastoral matters throughout your time at Queen Mary. You'll also take an active role in your own learning through independent study, reading, writing assignments and revising.
Where you'll learn
Facilities
Campus
Teaching will take place at our Mile End campus in the heart of East London. As one of the largest self-contained residential campuses in London, you'll be joining a friendly, vibrant and diverse community.
We have invested £105m in new facilities over the past five years to offer our students an exceptional learning environment. As well as the Mathematical Sciences building, this includes the new Graduate Centre, providing 7,700 square metres of learning and teaching space.
The campus is 15 minutes by tube from Central London, where you will have access to many of the University of London’s other facilities, such as Senate House.
About the School
School of Physical and Chemical Sciences
The School of Physical and Chemical Sciences (SPCS) is known for its world-leading research, and you’ll be learning from scientists at the forefront of their field. We’re also known for our outstanding teaching, which was recognised with a LearnSci Teaching Innovation award in 2021.
We’re a friendly, international and intellectually-curious community. And we’re looking forward to helping you thrive in your area of study and research.
Career paths
The demand for qualified AI and Machine Learning experts is growing and we are witnessing an explosion of AI opportunities in London. From environmental science to healthcare, organisations are seeking skilled graduates with hands-on coding and programming skills. Given the broad scope of this MSc and the focus on developing strong theoretical and practical skills, graduates may wish to consider other sectors such as communications, finance or retail.
Highly sought-after Machine Learning Engineers can earn over £60,000 per year in the UK (Source: Indeed).
Fees and funding
Full-time study
September 2025 | 1 year
- Home: £15,250
- Overseas: £29,950
EU/EEA/Swiss students
Conditional deposit
Home: Not applicable
Overseas: £2000
Information about deposits
Part-time study
September 2025 | 2 years
- Home: £7,800
- Overseas: £15,000
EU/EEA/Swiss students
Conditional deposit
Home: Not applicable
Overseas: £2000
Information about deposits
Queen Mary alumni can get a £1000, 10% or 20% discount on their fees depending on the programme of study. Find out more about the Alumni Loyalty Award
Funding
There are a number of ways you can fund your postgraduate degree.
- Scholarships and bursaries
- Postgraduate loans (UK students)
- Country-specific scholarships for international students
Our Advice and Counselling service offers specialist support on financial issues, which you can access as soon as you apply for a place at Queen Mary. Before you apply, you can access our funding guides and advice on managing your money:
Entry requirements
UK
Degree requirements
A good 2:2 (55% or above) or above at undergraduate level in Mathematics, Physics, Chemistry, Computing, Engineering or any other STEM subjects.
Find out more about how to apply for our postgraduate taught courses.
International
English language requirements
The English language requirements for our programmes are indicated by English bands, and therefore the specific test and score acceptable is based on the band assigned to the academic department within which your chosen course of study is administered. Note that for some academic departments there are programmes with non-standard English language requirements.
The English Language requirements for entry to postgraduate taught and research programmes in the School of Physical and Chemical Sciences falls within the following English band:
Band 4: IELTS (Academic) minimum score 6.5 overall with 6.0 in each of Writing, Listening, Reading and Speaking
We accept a range of English tests and qualifications categorised in our English bands for you to demonstrate your level of English Language proficiency. See all accepted English tests that we deem equivalent to these IELTS scores.
Visas and immigration
Find out how to apply for a student visa.