School of Electronic Engineering and Computer Science

Machine Learning for Visual Data Analytics

MSc ( 1 year Full-time / 2 years Part-time )


How can we design smartphones that sense your mood by reading your facial expressions or recognise hand gestures as a way to make a call? How do we develop systems that quickly and reliably analyse medical scans to assist with cancerous tumour diagnosis or improve the safety of self-driving cars with in-vehicle technology able to detect and modify a vehicle’s behaviour in any environment? These are just some of the fascinating questions that you will strive to answer on this programme.

This programme is intended to respond to a growing skills shortage in research and industry for engineers with a high level of training in the analysis and interpretation of images and video. It covers both low-level image processing and high-level interpretation using state-of-the-art machine learning methodologies.

In addition, it offers high-level training in programming languages, tools and methods that are necessary for the design and implementation of practical computer vision systems. You will be taught by world- class researchers in the fields of multimedia analysis, vision-based surveillance, structure from motion and human motion analysis. Aside from your lectures, you will be working on cutting-edge, live research projects, gaining hands-on experience.


Why study your MSc in Computer Science at Queen Mary?


Our research-led approach


Your tuition will be delivered by field leading academics engaged in world class research projects in collaboration with industry, external institutions and research councils.


Our strong links with industry

  • We have collaborations, partnerships, industrial placement schemes and public engagement programmes with a variety of organisations, including Vodafone, Google, IBM, BT, NASA, BBC and Microsoft
  • Full-time MSc with Industrial Experience option available on our taught MSc programmes. You have the option to complete over two years, with a year of work experience in industry.
  • Industrial projects scheme  - To support industrial experience development, you can to do your final project in collaboration with an industrial partner.


For questions of administrative nature, please contact

For questions about the academic content of the MSc Compuer Vision, please contact, quoting "MScVision" as the subject of your email.


Programme structure

MSc Machine Learning for Visual Data Analytics is currently available for one year full-time study, two years part-time study.


Undertaking a masters programme is a serious commitment, with weekly contact hours being in addition to numerous hours of independent learning and research needed to progress at the required level. When coursework or examination deadlines are approaching independent learning hours may need to increase significantly. Please contact the course convenor for precise information on the number of contact hours per week for this programme.


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. Teaching is generally done during the day and part-time students should contact the course convenor to get an idea of when these teaching hours are likely to take place. Timetables are likely to be finalised in September but you may be able to gain an expectation of what will be required.

Important note regarding Part Time Study

We regret that, due to complex timetabling constraints, we are not able to guarantee that lectures and labs for part time students will be limited to two days per week, neither do we currently support any evening classes. If you have specific enquiries about the timetabling of part time courses, please contact the MSc Administrator


Students take four modules in Semester 1 (two core and two optional) and four modules in Semester 2 (two core and two optional).

Semester 1

  • Machine Learning (15 credits)
  • Introduction to Computer Vision (15 credits)

Plus two options from:

  • Computer Graphics (15 credits)
  •  Big Data Processing (15 credits)
  • Data Mining (15 credits)

Semester 2

  • Deep Learning and Computer Vision (15 credits)
  • Machine Learning for Visual Data Analytics (15 credits)

Plus two options from:

  • Digital Media and Social Networks (15 credits)
  • Artificial Intelligence (15 credits)
  • Image Processing (15 credits)

Semester 3
(must take and pass)

  • Project (60 credits)

Below are examples of past MSc projects:

  • Automatic Road Segmentation
    This project, undertaken by a Yamaha Motors Ltd sponsored student, aimed at the identification of the location of the road on images taken from a moving vehicle. The project was based on machine learning methodologies, more specifically decision forests, for the classification of each local area according to features such as colour and motion. The developed method was tested on publicly available datasets on which it achieved state of the art results.
  • 3D face recostruction from a few images
    This project, aimed at the 3D reconstruction of a human face from a few images taken from depth-RGB cameras (such as Kinect cameras). It builds on general methodologies for reconstruction of general, rigid 3D objects and improves them by using information about the appearance and structure of the face. The results of face reconstruction can have applications in security (eg, for face recognition) or for face animation.



Entry requirements

An upper second class degree is normally required, usually in electronic engineering, computer science, maths or a related discipline. Students with a good lower second class degree may be considered on an individual basis. Applicants with unrelated degrees will be considered if there is evidence of equivalent industrial experience.

For international students we require English language qualifications IELTS 6.5 or TOEFL 92 (internet based).

International applicants

Students from outside of the UK help form a global community here at Queen Mary. For detailed country specific entry requirements please visit the International section of our website. If your first language is not English, you must provide evidence of your English language proficiency. You can find details on our English language entry requirements here:

If you do not meet language or scholarly requirements it might be possible for you to undertake foundation or pre-sessional programmes that will prepare you for the masters programme. For more information, please contact the Admissions Office.

Places on this programme are limited. We will give equal consideration to all applications received by May 1st 2019. We may still accept applications received after this date, but will not be able to offer a place if the programme is full.


Learning and teaching

As a student at Queen Mary, you will play an active part in your acquisition of skills and knowledge. Teaching is by a mixture of formal lectures and small group seminars. The seminars are designed to generate informed discussion around set topics, and may involve student presentations, group exercise and role-play as well as open discussion. We take pride in the close and friendly working relationship we have with our students. You are assigned an Academic Adviser who will guide you in both academic and pastoral matters throughout your time at Queen Mary.

All modules are taught through a combination of lectures and practical lab work. You can expect two to three hours of contact time per module, per week.

Independent study

For every hour spent in classes you will be expected to complete further hours of independent study. Your individual study time could be spent preparing for, or following up on formal study sessions; reading; producing written work; completing projects; and revising for examinations.

The direction of your individual study will be guided by the formal study sessions you attend, along with your reading lists and assignments. However, we expect you to demonstrate an active role in your own learning by reading widely and expanding your own knowledge, understanding and critical ability.

Independent study will foster in you the ability to identify your own learning needs and determine which areas you need to focus on to become proficient in your subject area. This is an important transferable skill and will help to prepare you for the transition to working life.


All modules are examined through a combination of coursework and written examinations taken in May/June. To obtain an MSc, you must gain passes in six of the eight modules taken with an overall average of 50 per cent. In addition to the above, the MSc requires that a satisfactory individual project be completed. If you do not pass the written examinations you will only be allowed to attempt the project after passing resit examinations the following May.


You will also be assessed on a supervised 10,000-15,000-word dissertation.

Our outstanding resources

  • We offer our students use of their own high- specification computing and research labs, hostings over 350 state-of-the-art computers for exclusive use by our students.
  • Our spectrum of research areas is supported by a range of specialist research labs offering cutting edge tools and technology including our augmented human interaction (AHI) laboratory combining pioneering technologies of  full-body and multi-person motion capture, virtual and augmented reality systems and advanced aural and visual display technologies.  We also have specialist laboratories in multimedia; telecommunication networks; and antenna measurement.

Have a look around by visiting our facilities pages for further information.


Tuition fees for Home and EU students

2020/21 Academic Year

Full time £10,900
Part time £5,450

Tuition fees for International students

2020/21 Academic Year

Full time £22,750
Part time £11,400

Part time fees are charged per annum over two years for a two year programme and per annum over three years for a three year programme. A percentage increase may be applied to the fees in years two and three.

This increase is defined each year and published on the intranet and in the Tuition Fee Regulations. A 3% increase was applied to the unregulated university fees in 2019/20. Further information can be viewed on our University Fees webpage, including details about annual increases.


There are a number of sources of funding available for Masters students.

These include a significant package of competitive Queen Mary University of London (QMUL) bursaries and scholarships in a range of subject areas, as well as external sources of funding.

Queen Mary bursaries and scholarships

We offer a range of bursaries and scholarships for Masters students including competitive scholarships, bursaries and awards, some of which are for applicants studying specific subjects.

Find out more about QMUL bursaries and scholarships.

Alternative sources of funding

Home/EU students can apply for a range of other funding, such as Professional and Career Development Loans, and Employer Sponsorship, depending on their circumstances and the specific programme of study.

Overseas students may be eligible to apply for a range of external scholarships and we also provide information about relevant funding providers in your home country on our country web pages.

Download our Postgraduate Funding Guide for detailed information about postgraduate funding options for Home/EU students.

Read more about alternative sources of funding for Home/EU students and for Overseas students.

Tel: +44 (0)20 7882 5079

Other financial help on offer at Queen Mary

We offer one to one specialist support on all financial and welfare issues through our Advice and Counselling Service, which you can access as soon as you have applied for a place at Queen Mary.

Our Advice and Counselling Service also has lots of Student Advice Guides on all aspects of finance including:

Tel: +44 (0)20 7882 8717

Graduate employment

Your skills and knowledge will be valuable in all industries that require intelligent processing and interpretation of image and video. This includes industries in directly related fields, such as multimedia indexing and retrieval (eg, Google, Microsoft), motion capture (eg, Vicon), media production (eg, Sony, Technicolor, Disney), medical imaging, security and defence (eg, Qinetiq), robotics, and industries in related areas that require good knowledge of machine learning, signal processing and programming.

A number of our academics have common research projects with industrial partners such as Disney, BBC, Technicolor and ST Microelectronics, and take on consultancy work with industry.

Our staff draws on this industry experience to inform and enrich their teaching, bringing theoretical subjects to life. In addition, several of the final year projects are offered in collaboration with research institutes or industrial partners (

You will also be ideally placed to pursue further research, including continuing onto PhD studies. We will award two PhD fee waivers for top ranked students in this MSc who desire to continue into our PhD programme.