As recent developments in computers and sensors make the generation, storage and processing of visual data easier, methods that enable a machine to analyse and understand images and videos become increasingly relevant. The advances in this field are behind Google's autonomous vehicles, Meta's image analysis technologies and car plate recognition systems.
This programme is designed to train engineers to work in the analysis and interpretation of images and video.
- Undertake high-level training in programming languages, tools and methods necessary for the design and implementation of practical computer vision systems.
- Be taught by world-class researchers in the fields of multimedia analysis, vision-based surveillance, structure from motion and human motion analysis.
- Work on cutting-edge research projects, gaining hands-on experience.
Study options
- Starting in
- September 2025
- Location
- Mile End
- Fees
- Home: £12,850
Overseas: £29,950
EU/EEA/Swiss students
What you'll study
This course will enable students to study cutting-edge technologies in the field of machine learning for visual analytics, and will provide them with the background and skills they need to pursue careers in research or industry. Course content covers:
- Fundamental methods and techniques in computer vision, machine learning and image processing.
- Programming tools, languages and techniques for the application of machine learning methods to analyse visual data.
- Methods and techniques for systems and applications.
The programme is taught by academics from the Computer Vision and Multimedia and Vision research groups. The groups consist of a team of more than 100 researchers (academics, post-docs, research fellows and PhD students) performing world-leading research into the fields of surveillance, face and gesture recognition, multimedia indexing and retrieval and robotics.
The School has collaborations, partnerships, industrial placement schemes and public engagement programmes with organisations including Vodafone, Google, IBM, BT, NASA, BBC and Microsoft.
This degree is accredited by BCS, The Chartered Institute for IT, for the purposes of partially meeting the academic requirement for registration as a Chartered IT Professional. This degree is also accredited by BCS on behalf of the Engineering Council, for the purposes of partially meeting the academic requirement for registration as a Chartered Engineer.
Structure
- Eight modules divided into two streams; students choose one stream to follow once they start in September.
- Compulsory final project module (60 credits).
Vision and Language stream
Semester 1
Machine Learning
Introduction to Computer Vision
Computer Programming
Natural Language Processing
Semester 2
Deep Learning and Computer Vision
Machine Learning for Visual Data Analysis
Neural Networks and NLP
Neural Networks and Deep Learning
Semester 3
Project Module
Computer Vision and Image Processing stream
Semester 1
Machine Learning
Introduction to Computer Vision
Computer Programming
Computer Graphics
Semester 2
Deep Learning and Computer Vision
Machine Learning for Visual Data Analysis
Image Processing
Neural Networks and Deep Learning
Semester 3
ECS750P - Project Module
These streams will allow you to further develop your professional profile and graduate with industry-relevant expertise.
Find out more about each module by looking them up in the module directory.
Please note that all modules are subject to change.
Assessment
- 67% Modules
- 33% Research project
Modules are assessed through a combination of coursework and written examinations. You will also be assessed through your final research project.
Research project
You will devise and carry out a major final project, which you may do in collaboration with an industrial partner.
Examples of past project topics include:
- Automatic road segmentation - sponsored by Yamaha Motors Ltd.
- 3D face reconstruction from a few images.
- Sentiment analysis in images.
—I chose my MSc because computer vision was the most appealing field in machine learning for me, and Queen Mary has an excellent computer vision department. I most enjoyed my modules in Introduction to Computer Vision with Professor Andrea Cavallaro and Deep Learning and Computer Vision with Professor Sean Gong -they’re both fantastic lecturers, really knowledgeable and engaging. During my MSc, I gained important skills in machine learning and computer vision and experience conducting research projects, designing experiments and critically analysing academic papers. The video presentation I made as part of my research project got me the interview which eventually led to my role as a Computer Vision Engineer.
Tom Hughes, MSc Machine Learning for Visual Data Analytics, 2023
Teaching
Students of this course can expect to take part in:
- Lectures, in which the theory and algorithms are presented.
- Practical sessions and labs, where they can get hands-on experience with tools and algorithms.
Each module provides contact time with your lecturers, supported by lab work and self-directed further study.
You will be assigned an Academic Advisor who will guide you in both academic and pastoral matters throughout your time at Queen Mary.
Part-time study options often mean that the number of modules taken is reduced per semester, with the full modules required to complete the programme spread over two academic years.
Where you'll learn
Facilities
The School has excellent bespoke facilities, including:
- Augmented human interaction (AHI) laboratory
- Informatics teaching laboratory with 350 state-of-the-art computers
- Antenna measurement laboratory
- qMedia and arts technology studios (Performance lab, control room, listening/recording room)
- Robotics laboratory (ARQspace)
Campus
Teaching is based at Queen Mary’s main Mile End campus, one of the largest self-contained residential campuses in the capital. Our location in the heart of London’s East End offers a rich cultural environment.
We have invested £105m in new facilities over the past five years to offer our students an exceptional learning environment. Recent developments include the £39m 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 facilities, such as the Senate House library.
About the School
School of Electronic Engineering and Computer Science
The School of Electronic Engineering and Computer Science carries out world-class research – and applies it to real-world problems. Being taught by someone who is changing the world with their ideas makes for exciting lectures, and helps you to stay ahead of the curve in your field. 99 per cent of our research is classed as ‘world-leading’ or ‘internationally excellent’ (REF 2021).
We are proud of our excellent student-staff relations, and our diverse student body, made up of learners from more than 60 countries.
The School has a close-knit student community, who take part in competitions and extracurricular lab activities.
Career paths
- 100% In highly skilled jobs 15 months after graduation.
Fees and funding
Full-time study
September 2025 | 1 year
- Home: £12,850
- Overseas: £29,950
EU/EEA/Swiss students
Conditional deposit
Home: £2000
Overseas: £2000
Information about deposits
Part-time study
September 2025 | 2 years
- Home: £6,450
- Overseas: £15,000
EU/EEA/Swiss students
Conditional deposit
Home: £2000
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 2:1 or above at undergraduate level in Electronic Engineering, Computer Science, Software Engineering, Information Technology, Mathematics or a related discipline.
Other routes
Applicants with a good 2:2 degree (55% or above) will be considered on an individual basis.
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 Electronic Engineering and Computer Science 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.