Develop advanced expertise with our MSc Machine Learning degree. This one-year course focuses on both the theory and practice of modern artificial intelligence, including deep learning and reinforcement learning. You'll develop a strong mathematical foundation and learn how to turn data and algorithms into working systems that solve real-world problems.
This machine learning master’s degree is for you if you already have experience in computing, mathematics or a related subject and want to specialise in AI methods and models. You'll study alongside other motivated postgraduates and learn from teaching informed by current research in areas such as natural language processing, computer vision and optimisation.
You'll start by studying core concepts that underpin machine learning, including probability, linear algebra and statistical learning theory. You'll then explore advanced topics such as deep neural networks, Bayesian methods, active learning and reinforcement learning.
You'll also learn how to design scalable, efficient models that work with large and complex data sets. Core teaching is supported by specialist optional modules so you can tailor the course to your interests.
On this machine learning course you'll:
Through optional modules you'll be able to focus on areas that match your interests and goals. These may include topics such as data mining, natural language processing, computer vision or optimisation for machine learning, depending on current research and teaching expertise in the department.
Your individual research project is a major part of the MSc. You'll work with an academic supervisor on an in-depth investigation, such as developing or evaluating a new method, or studying how a particular machine learning model makes decisions.
Through this project you'll build practical experience of planning and delivering a research study, from the initial question through to the final report. You'll build a portfolio of work you can use to support applications for PhD study or specialist roles in industry, and prepares you to contribute to future developments in the field.
We regularly review our courses to ensure and improve quality. This course may be revised as a result of this. Any revision will be balanced against the requirement that the student should receive the educational service expected. Find out why, when, and how we might make changes.
Our courses are regulated in England by the Office for Students (OfS).
The programme leader for this course is Antonia Marcu.
This course is based at Highfield.
This qualification is awarded by the University of Southampton.
The Course Description Document details your course overview, your course structure and how your course is taught and assessed.
You’ll need a 2:1 degree, or equivalent, in an undergraduate (bachelor’s or integrated master’s) course that includes a significant amount of mathematics and some computing and programming.
We expect you to have studied topics such as linear algebra and calculus, and to have experience of programming, ideally using Python. Degrees in computer science, physics, engineering or mathematics usually meet these requirements. We also welcome applications from other subjects if they include significant maths and computing content.
This programme only accepts applicants who have studied at a Tier A, B or C institution.
If English is not your first language, you must show that you can use English to the level we require. Visit our English language pages to find out which qualifications we accept and how you can meet our requirements.
If you are taking the International English Language Testing System (IELTS), you must get at least the following scores:
If you do not meet the English language requirements through a test or qualification, you may be able to meet them by completing one of our pre-sessional English programmes before you start your course.
If you don’t meet direct entry requirements, you can apply to complete a Pre-Master's programme through our partnership with OnCampus.
You'll progress to your chosen course after successfully completing the programme. Find out more about Pre-Master's programmes for international students.
Please contact us if you're not sure you have the right experience or qualifications to get onto this course.
Email: enquiries@southampton.ac.uk
Tel: +44(0)23 8059 5000
This MSc Machine Learning degree is a one-year, full-time course made up of 8 taught modules and an independent research project. You’ll study on campus over 3 semesters and complete the programme within 12 months. The structure combines core training in machine learning theory with options that let you focus on specialist topics before you move on to your research dissertation.
In Semester 1, you’ll take 3 compulsory modules plus Research Methods and Project Preparation. These core modules cover the foundations you need for advanced study, including the mathematics of machine learning, key algorithms and models, and an introduction to deep learning. Research Methods and Project Preparation runs across both teaching semesters and helps you plan and design your later project work.
In Semester 2, you’ll choose 4 optional modules, alongside the continuing Research Methods and Project Preparation module. With optional modules you'll tailor this machine learning degree to your interests. You can specialise in areas such as:
Your choices will help you prepare for your summer project and for future roles or research.
In the summer, you’ll complete an independent research project over 3 to 4 months, supported by an academic supervisor. You’ll investigate a specific problem in machine learning or an application area such as healthcare, finance or autonomous systems, and report your findings in a 15,000-word dissertation.
Want more detail? See all the modules in the course.
The modules outlined provide examples of what you can expect to learn on this degree course based on recent academic teaching. As a research-led University, we undertake a continuous review of our course to ensure quality enhancement and to manage our resources. The precise modules available to you in future years may vary depending on staff availability and research interests, new topics of study, timetabling and student demand. Find out why, when and how we might make changes.
You must study the following modules :
Machine Learning is about extracting useful information from large and complex datasets. The subject is a rich mixture of concepts from function analysis, statistical modelling and computational techniques. The module will cover the fundamental principles...
Differentiable Programming and Deep learning has revolutionised numerous fields in recent years. We’ve witnessed improvements in everything from computer vision through speech analysis to natural language processing as a result of the advent of cheap GPGP...
Your research project will enable you to explore in depth some aspect of your specialist subject area. You will be allocated a project supervisor with whom you will meet and agree a project brief and plan. These must be submitted to, and agreed by, ...
Machine Learning is about extracting useful information from large and complex datasets. Although driven by applications, the techniques used are based on a broad mathematical basis. This course provides the mathematical foundations of the subject from f...
The Module will prepare students for the summer Research Project and research based coursework exercises. It will give students a grounding in the research methods and techniques necessary in order for planning and successful execution and completion of t...
You must also choose from the following modules :
This module: - Introduces the students to the key issues of interaction of multiple self-interested parties (a.k.a. agents) and gives a broad survey of topics at the interface of theoretical computer science and game theory dealing with such interactions...
To gain an in-depth theoretical and practical understanding of Bayesian inference and active learning, and their applications.
Biometrics is about how we can recognise people automatically, by personal characteristic. We all have fingerprints and faces - and they are unique. We have to sense the information, process it and then deliver an assessment of the identity associated wit...
The broad educational goal is to enhance students' ability to reason about data generating processes, and extract, interrogate and evaluate patterns in the data. The focus on methods of causal analysis overlaps with the objectives of ``explainable AI." St...
The challenge of computer vision is to develop a computer based system with the capabilities of the human eye-brain system. It is therefore primarily concerned with the problem of capturing and making sense of digital images. The field draws heavily on ma...
The challenge of data mining is to transform raw data into useful information and actionable knowledge. Data mining is the computational process of discovering patterns in data sets involving methods at the intersection of artificial intelligence, machine...
Deep learning has revolutionised numerous fields in recent years. We've witnessed improvements in everything from computer vision through speech analysis to natural language processing. This module focuses on the latest advances in deep learning and will ...
Evolution by natural selection has created amazingly complex and sophisticated solutions to some very difficult problems - how exactly does it achieve this, and how can we harness this capability for engineering artificial systems and computational proble...
This module gives students an introduction to natural language processing (NLP) algorithms and an understanding of how to implement NLP applications.
This module is about the fundamentals of algorithms solving continuous optimisation problems, which involve minimising functions of multiple real-valued variables, possibly subject to restrictions, constraints, and nondifferentiable regularisations on the...
You’ll learn through a mix of timetabled teaching and independent study. Teaching includes lectures that introduce key ideas, supported by practical classes and other guided activities that help you practise using machine learning methods in context.
Your overall workload combines:
Your learning is led by current research in machine learning and artificial intelligence. As you progress, you’ll move from learning core concepts to applying and evaluating advanced models on realistic problems, building towards your individual research project.
You’ll also take a Research Methods and Project Preparation module, which runs across the teaching semesters. This helps you plan your project, develop research skills and learn how to present technical work clearly.
The course uses a combination of formative and summative assessment. Formative activities help you check your understanding and get feedback as you go, without contributing to your final marks. Summative assessments contribute to your module and degree results.
Assessment methods include:
Full details of assessment methods and weightings are provided in the information for each module.
In the summer you’ll complete an independent research project over 3 to 4 months, supervised by an academic in the department. You’ll investigate a specific machine learning question or application area, design and carry out your study, and present your work in a dissertation of around 15,000 words. This brings together the skills and knowledge you’ve developed on the course and gives you experience of doing research in depth.
You’ll be taught by an experienced team of academics and researchers, with additional input from specialist staff where appropriate. You’ll also have access to University-wide support, including the Student Support Hub, which can help with study, wellbeing and practical issues. A member of academic staff will act as a key contact for your studies, and you’ll receive supervision during your research project.
The employability and enterprise skills you'll gain from this course are reflected in the Southampton skills model. When you join us you'll be able to use our skills model to track, plan, and benefit your career development and progress.
Download skills overview
Choosing to do work experience is a great way to enhance your employability, build valuable networks, and evidence your potential. Learn about the different work and industry experience options at Southampton.
We are a top 20 UK university for employability (QS Graduate Employability Rankings 2022). Our Careers, Employability and Student Enterprise team will support you. This support includes:
We have a vibrant entrepreneurship culture and our dedicated start-up supporter, Futureworlds, is open to every student.
Your career ideas and graduate job opportunities may change while you're at university. So it is important to take time to regularly reflect on your goals, speak to people in industry and seek advice and up-to-date information from Careers, Employability and Student Enterprise professionals at the University.
Fees for a year's study:
If you're an international student on a full-time course, we'll ask you to pay £2,000 of your tuition fees in advance, as a deposit.
Your offer letter will tell you when this should be paid and provide full terms and conditions.
Find out about exemptions, refunds and how to pay your deposit on our tuition fees for overseas students page.
Your tuition fee covers the full cost of tuition and any exams. The fee you pay will remain the same each year from when you start studying this course. This includes if you suspend and return.
Find out how to pay your tuition fees.
Accommodation and living costs, such as travel and food, are not included in your tuition fees. There may also be extra costs for retake and professional exams.
Explore:
If you’re a graduate of the University of Southampton, you could be eligible for a 10% discount on your postgraduate tuition fees.
This can help with course fees and living costs while you study a postgraduate master's course.
If you study part-time, you may not be eligible for this loan. Find out if you're eligible.
A variety of additional funding options may be available to help you pay for your master’s study. Both from the University and other organisations.
Find out about funding you could get as an international student.
For further details of our admission process, read our step by step guide to postgraduate taught applications.
The deadline to apply for this course is Wednesday 2 September 2026, midday UK time.
We advise applying early as applications may close before the expected deadline if places are filled.
The deadline to apply for this course is Wednesday 19 August 2026, midday UK time.
We advise applying early as applications may close before the expected deadline if places are filled.
There is no application assessment fee for postgraduate courses starting in 2026.
When you apply you’ll need to submit a personal statement explaining why you want to take the course.
You’ll need to include information about:
You'll also need to submit two academic references.
Please include the required paperwork showing your first degree and your IELTS English language test score (if you are a non-native English speaker) with your application. Without these, your application may be delayed.
You'll be able to track your application through our online Applicant Record System.
We will aim to send you a decision 6 weeks after you have submitted your application.
If we offer you a place, you will need to accept the offer within 30 working days. If you do not meet this deadline, we will offer your place to another applicant.
Unfortunately, due to number of applications we receive, we may not be able to give you specific feedback on your application if you are unsuccessful.
We treat and select everyone in line with our Equality and Diversity Statement.
Please contact us if you're not sure you have the right experience or qualifications to get onto this course.
Email: enquiries@southampton.ac.uk
Tel: +44(0)23 8059 5000
