Statistics with Artificial Intelligence MSc

Level: Postgraduate taught

Subject: Mathematics and Statistics

Year of entry: 2027

Study options

Mode of study

On campus

Part-time
  • MSc | 2 years | Start date: September 2027
Full-time
  • MSc | 1 year | Start date: September 2027

Key facts

School

School of Mathematics

College

College of Science and Engineering

Study location

King's Buildings campus

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About the degree programme

Artificial intelligence is increasingly used in high-stakes settings, from healthcare and finance to government and technology. In these environments, employers need people who can do more than build models. They need people who can evaluate outputs carefully, understand uncertainty, and judge when AI systems are reliable enough to support real decisions.

This MSc is a statistics-first route into AI. It provides the statistical foundations that make AI work and make it trustworthy. You will study probability, statistical inference, Bayesian modelling, machine learning and optimisation, with dedicated study of deep learning. You will also learn how to assess AI outputs as statistical evidence, with attention to uncertainty, assumptions and limitations.

The programme is taught hands-on in Python and R. You will develop practical fluency in reproducible computational workflows for statistical and machine learning analysis, together with a critical understanding of calibration, robustness, distribution shift, subgroup effects, fairness, data provenance and governance in AI-related settings. This prepares you for roles in applied machine learning, model validation, AI teams and other areas where statistical judgement matters.

A major feature of the programme is its consultancy-style dissertation format, based on a proven model already used successfully across related programmes in the School's Statistics MSc portfolio. In the summer, you will complete two case projects in different application areas, giving you the chance to apply statistical and AI methods to realistic briefs involving machine learning, uncertainty, model evaluation and decision-ready evidence, build breadth across more than one domain, and develop the communication skills needed in professional practice.

Edinburgh offers a distinctive environment for this training through the School of Mathematics, with close links to internationally recognised AI research and teaching elsewhere in the University, including Informatics, and to the wider data-driven innovation ecosystem through the Bayes Centre.

Study options

This programme can be studied full-time over 1 year or part-time over 2 years.

Part-time study

If you study part-time, you will take half of the taught credits in your first year, and the other half in your second year. You will complete the final project full-time in the summer at the end of your second year.

Our large course offering means that taught courses have contact times throughout the week, and you are expected to be on campus during these times.

Tuition fees

Tuition fees by award and duration

Tuition fees for full-time and part-time options are listed for one academic year.

Part-time
Full-time

Graduate discount

If you are a University of Edinburgh graduate, you will be eligible for a 10% discount on your tuition fees for this programme. You may also be eligible if you were a visiting undergraduate student.

Find out how to receive your graduate discount

Deposit

You do not have to pay a deposit to secure your place on this programme.

Costs

Accommodation and living costs

You will need to pay for your accommodation and living costs (such as rent, food and utility bills) for the duration of your programme.

For 2026-2027, we estimate that it will cost an average of £18,504 each year (£1,542 each month) to live in Edinburgh as a postgraduate.

The exact amount you spend will depend on different factors, especially the accommodation you choose.

Living costs: estimates explained 

Funding opportunities

Funding for postgraduate study is different to undergraduate study, and many students need to combine funding sources to pay for their studies.

Most students use a combination of the following funding to pay their tuition fees and living costs:

  • borrowing money
    • taking out a loan
    • family support
  • personal savings
  • income from work
  • employer sponsorship
  • scholarships

Explore sources of funding for postgraduate study

The College of Science and Engineering is offering an international scholarship for the most academically gifted postgraduate taught (PGT) masters students applying from France, Germany and Spain. These awards, which cover £10,000 towards tuition fees for PGT masters students studying on campus will be tenable for the first year of study only. 

Value: £10,000
View details: Find out more about the European Merit Scholarships

The University is offering an international scholarship for the most academically gifted postgraduate taught (PGT) masters students applying from India. These awards, which cover £5,000 towards tuition fees for PGT masters students studying on campus will be tenable for the first year of study only.

Value: £5,000
View details: Find out more about the India Merit Scholarships

Search for other funding opportunities

You can find scholarships, bursaries and other funding you might be eligible for on our Scholarships and Student Funding site.

Postgraduate funding opportunities

These entry requirements are for the 2027-28 academic year and requirements for future academic years may differ. Entry requirements for the 2028-29 academic year will be published in early Oct 2027.

Qualifications

A UK 2:1 honours degree, or international equivalent, in a quantitative discipline such as mathematics, statistics, physics, engineering, computer science, economics, or quantitative biosciences.

You must have studied substantial mathematics content, including calculus, linear algebra, probability, and statistics, and you should have basic proficiency in Python or R.

You can increase your chances of a successful application by exceeding the minimum programme requirements.

International equivalent qualifications

To find international equivalent qualifications, select where you studied from the country or region list.

English language requirements

You must prove that your English language abilities are at a high enough level to study this degree programme.

This is the case for all applicants, including UK nationals.

You can meet our English language requirements with one of the following:

  • an English language test (restrictions apply)
  • a degree that was taught and assessed in English (restrictions apply)
  • certain professional qualifications

English language tests we accept

We accept any of the following English language tests, at the specified grade or higher:

  • IELTS Academic: total 6.5 with at least 6.0 in each component. We do not accept IELTS One Skill Retake to meet our English language requirements.
  • TOEFL-iBT (including Home Edition) before 21 January 2026: total 92 with 23 in each component. We do not accept TOEFL MyBest Score to meet our English language requirements.
  • TOEFL-iBT (including Home Edition) from 21 January 2026: total 4.5 with at least 4.5 in each component. We do not accept TOEFL MyBest Score to meet our English language requirements.
  • C1 Advanced (CAE) / C2 Proficiency (CPE): total 176 with at least 169 in each component.
  • Oxford ELLT: total 7 with at least 6 in each component. We do not accept Oxford ELLT Skill Retake to meet our English language requirements.
  • Oxford Test of English Advanced: total 145 with at least 135 in each component.
How old your English language tests can be
Tests no more than two years old

The following English language tests must be no more than two years old on the 1st of the month in which your programme starts, regardless of your nationality:

  • IELTS Academic
  • TOEFL-iBT (including Home Edition)
  • Oxford ELLT
  • Oxford Test of English Advanced
Tests no more than three and a half years old

All other English language tests must be no more than three and a half years old on the 1st of the month in which your programme starts, regardless of your nationality.

Degrees taught and assessed in English

We accept an undergraduate or postgraduate degree that has been taught and assessed in English in a majority English-speaking country, as defined by UK Visas and Immigration.

UKVI list of majority English speaking countries

We also accept a degree that has been taught and assessed in English from a university on our list of approved universities in non-majority English-speaking countries (non-MESC).

Approved universities in non-MESC

How old your degree can be

If you are not a national of a majority English-speaking country, then your degree must be no more than five years old on the 1st of the month in which your programme starts.

This time limit does not apply to your degree if you are a national of a majority English-speaking country.

Find out more about our English language requirements

Find out about other English language qualifications we accept, including professional qualifications.

English language requirements

What you will study

The MSc comprises 180 credits. You will complete 120 taught credits and a 60-credit dissertation.

The current planned structure is:

  • 80 credits of compulsory courses
  • 40 credits of option courses
  • 60-credit dissertation

Across two taught semesters, you will build a strong foundation in statistical modelling for AI, machine learning, optimisation, data design and sampling, and reproducible computational practice, with dedicated study of deep learning. You will also develop the judgement needed to evaluate AI systems responsibly and communicate results clearly.

In the summer, you will complete a consultancy-style dissertation format based on two case projects in different application areas. This brings together technical, statistical and communication skills in an applied setting.

Compulsory courses

Compulsory courses are expected to include:

  • Bayesian Data Analysis
  • Bayesian Theory
  • Deep Learning
  • Design and Sampling for Data Science
  • Fundamentals of Optimization
  • Machine Learning in Python
  • Statistical Programming
  • Statistical Research Skills

Option courses

Option courses are expected to be drawn primarily from the School of Mathematics. Option courses from the School of Mathematics are expected to include:

  • Biostatistics
  • Fundamentals of Operational Research
  • Generalised Regression Models
  • Incomplete Data Analysis
  • Large-Scale Optimization for Data Science
  • Multivariate Data Analysis
  • Nonlinear Optimization
  • Numerical Methods for Data
  • Simulation
  • Stochastic Modelling
  • Time Series

Subject to annual availability and approval, you may also be able to take a limited number of quota-controlled options from the School of Informatics and other relevant University offerings, including ethics in AI.

Option courses may change from year to year.

Teaching and assessment

Teaching

Teaching methods include lectures, practical labs, workshops, coursework-based learning, formative quizzes, independent study and supervised dissertation work.

The programme combines mathematical and statistical foundations with hands-on applied work. You will use Python and R throughout the programme and work with reproducible computational workflows as a normal part of study.

Assessment

Assessment across the programme includes written examinations, programming exercises, quizzes, practical tasks, reports and the dissertation. The balance varies by course, but the overall mix is designed to assess theoretical understanding, technical implementation and communication.

You will be supported by standardised teaching materials, shared computing environments and common reproducibility templates across core courses. Where appropriate, centrally provided computing resources, including managed GPU access, will support project work.

Dissertation

The dissertation will take the form of two consultancy-style case projects in different application areas.

Each consultancy-style project will typically last five to six weeks and will take the form of a consultant-client model in which an industrial or external client presents a problem to the class. You will then work as a consultant on an aspect of the problem and present your conclusions at the end of the project.

This format gives you the chance to work with real data, real constraints and real communication challenges. It also helps you build experience in framing applied problems, choosing appropriate statistical and AI methods, judging uncertainty carefully and explaining results in a way that others can use.

This is one of the programme's most distinctive features and a major opportunity to build confidence, employability and applied problem-solving skills.

This builds on established practice across the School's Statistics MSc portfolio. Recent externally linked project work on related programmes has included:

  • Lloyds Banking Group - "Network Security: Exploring the use of  autoencoders and variational autoencoders for anomaly detection in cyber security"
  • Public Health Scotland - "Understanding the impact of age demographics on hospital admissions in Scotland"
  • Environment Agency - "Temporal trends in freshwater macroinvertebrate abundance: combining numerical and categorical  data using mgcv's grouped family (gfam)"
  • Space Intelligence - "Filling the gaps: Using synthetic aperture radar data to reconstruct multispectral images"
  • Simply Business - "Property insurance risk assessment: using public data to estimate building features that affect insurance value"
  • Xi Engineering - "Removing seismic noise (background) from wind turbine impact measurement datasets"

Learning outcomes

At the end of this programme you will be able to:

  • demonstrate understanding of probability, statistical inference, Bayesian modelling and model-based reasoning
  • explain and evaluate supervised and unsupervised machine learning methods, optimisation and model evaluation
  • evaluate data design, sampling, provenance and sources of statistical bias
  • assess uncertainty quantification, calibration, robustness, distribution shift, fairness and reproducibility in AI-related settings
  • explain the role of governance, ethics and communication in trustworthy statistical and AI practice
  • build, fit, critique and compare statistical and machine learning models
  • implement Bayesian inference and hierarchical models using modern computational tools
  • quantify and communicate uncertainty and model calibration
  • assess robustness, distribution shift and subgroup or fairness considerations where relevant
  • create reproducible analyses and computational environments in Python and R
  • produce clear technical reports and decision-focused outputs for applied settings
  • communicate technical findings, assumptions and limitations to technical and non-technical audiences

Support for your studies

Students will be supported through a structured induction and advising framework. Induction will include academic orientation, computing and platform setup, and guidance on expectations for reproducibility and assessment.

The programme uses the School of Mathematics Student Adviser model and standard School support arrangements. The Programme Director and teaching staff will provide academic guidance, and dissertation briefings will support the transition from taught study to project work.

Students will also be signposted to optional pre-sessional refresher materials in programming and mathematics.

Where you will study

Study location

Teaching for School of Mathematics courses will take place at the King's Buildings campus, usually in the James Clerk Maxwell Building (JCMB).

You will study using lecture theatres, teaching studios, laptop-first practical labs, University online learning systems, and centrally provided computing resources, including managed GPU access where appropriate.

Library support is provided through Leganto resource lists, e-books and standard University digital library access.

The MScHub in JCMB is specifically for School of Mathematics MSc students, and offers a dedicated space for studying and socialising, including its own kitchen facilities.

Academic facilities

Full details of the facilities available at King’s Buildings (including libraries, study spaces and catering outlets) are available on the College of Science and Engineering website:

Facilities at King's Buildings

Career opportunities

Graduates from this programme will be equipped to build and evaluate AI systems that stand up to scrutiny.

Roles may include:

  • Data Scientist
  • AI Scientist
  • Applied Machine Learning Scientist
  • Statistician in AI teams
  • Biostatistician
  • Health Data Scientist
  • Risk or Model Validation Analyst
  • Public-sector Decision Scientist

The programme develops highly transferable strengths in uncertainty, evaluation, reproducibility, programming and communication. These are especially valuable in regulated and high-stakes sectors such as health, finance, government and technology.

Professional links

Edinburgh offers a distinctive environment for this programme through its connections across statistics, machine learning and artificial intelligence.

The programme has close links to AI research and teaching elsewhere in the University, including Informatics, and sits within the wider data-driven innovation ecosystem of the Bayes Centre.

Across the School's Statistics MSc portfolio, externally linked project work has included organisations such as Lloyds Banking Group, Public Health Scotland, Environment Agency, Space Intelligence, Simply Business and Xi Engineering. These examples show the range of settings in which statistical and AI methods can be applied, from cybersecurity and public health to environmental analysis, insurance and engineering.

Students will also benefit from careers and employability activities associated with the School and the Bayes Centre.

Further study

After completing this programme, you may wish to consider applying for a PhD or other research programme.

Applying for research degrees

Moving on to a PhD (advice from the University's Careers Service)

Graduate profiles

While this MSc is new, you can browse recent student experience and graduate profiles from related Statistics MSc programmes on the School website.

Student experience

 

Careers Service

Our Careers Service can help you to fully develop your potential and achieve your future goals. 

The Careers Service supports you not only while you are studying at the University, but also for up to two years after you finish your studies. 

With the Careers Service, you can: 

  • access digital resources to help you understand your skills and strengths
  • try different types of experiences and reflect on how and what you develop
  • get help finding work, including part-time jobs, vacation work, internships and graduate jobs
  • attend careers events and practice interviews
  • get information and advice to help you make informed decisions 

Visit the Careers Service website 

How to apply

You apply online for this programme. After you read the application guidance, select your preferred programme, then choose 'Start your application' to begin.

If you are considering applying to more than one programme, you should be aware that we cannot consider more than 3 applications from the same applicant.

When to apply

Due to high demand, the school operates a number of selection deadlines.

We strongly recommend you apply as early as possible. Applications may close earlier than the published deadlines if there is exceptionally high demand.

We may make a small number of offers to the most outstanding candidates on an ongoing basis. However, the majority of applications will be held until the advertised deadline.

We aim to make the majority of decisions within eight weeks of the selection deadline.

If we have not made you an offer by a specific selection deadline this means one of two things:

  • your application has been unsuccessful, in which case we will contact you to let you know
  • your application is still being considered, it will be carried forward for consideration in the next selection deadline, and we will be in touch once a decision is made

The final deadline may be extended if any places remain on the programme.

If the final deadline is extended, we encourage you to apply at least one month prior to entry so that we have enough time to process your application. If you are also applying for funding or will require a visa then we strongly recommend you apply as early as possible.

Selection deadlines

RoundApplication deadlineDecisions made or applications rolled to the next deadline
115 December1 March
231 March31 May
331 May31 July

When to submit your supporting documents

You must submit all supporting documents by the application deadline, or we will be unable to consider your application. Regardless of when you apply, you have 28 days from submitting your application to supply any supporting documents through the Application Hub, after which we will automatically reject your application.

Application fee

There is no fee to apply to this programme.

What you need to apply

As part of your online application, you will need to provide: 

You will also need to submit some or all of the following supporting documents:

When you start your application, you will be able to see the full list of documents you need to provide.

Apply

Select the award, duration and delivery mode you want to study. Then select the start date you want to apply for.

After you apply

Once you have applied for this programme, you will be able to track the progress of your application and accept or decline any offers.

Checking the status of your application

We will notify you by email once we have made a decision. Due to the large number of applications we receive, it might take a while until you hear from us.

Receiving our decision

What to do if you receive an offer:

What our students say

Student blogs

Learn about what life is like as a MSc student in the School of Mathematics by hearing from the people that have experienced it first-hand! Our student bloggers come from all across the world, and have studied on a variety of our MSc programmes.

Postgraduate blogs

Accommodation

We guarantee an offer of University accommodation for all new, single postgraduate taught students from outside the UK and new, single postgraduate research (typically PhD) students who:

  • apply for accommodation by 31 July in the year when you start your programme
  • accept an unconditional firm offer to study at the University by 31 July
  • study at the University for the whole of the academic year starting in September

University accommodation website

Accommodation guarantee criteria

We also offer accommodation options for couples and families.

Accommodation for couples and families

If you prefer to live elsewhere, we can offer you advice on finding accommodation in Edinburgh.

Accommodation information from the Edinburgh University Students' Association Advice Place

Societies and clubs

Our societies and sports clubs will help you develop your interests, meet like-minded people, find a new hobby or simply socialise.

Societies

Sport Clubs

The city of Edinburgh

Scotland's inspiring capital will form the background to your studies — a city with an irresistible blend of history, natural beauty and modern city life. 

Find out more about living in Edinburgh

Health and wellbeing support

You will have access to free health and wellbeing services throughout your time at university if you need them.

The support services we offer include: 

  • a student counselling service
  • a health centre (doctor's surgery)
  • support if you're living in University accommodation
  • dedicated help and support if you have a disability or need adjustments

Health and wellbeing support services 

Disability and Learning Support

General enquiries

Admissions
School of Mathematics
James Clerk Maxwell Building
The King's Buildings Campus
Edinburgh
EH9 3FD

futurestudents@ed.ac.uk

Academic contact

Dr Ioannis Papastathopoulos

Programme Director

Contact details

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