Taught course

Machine Learning for Visual Data Analytics

Queen Mary, University of London · Computer Science

Entry requirements

A 2:1 or above at undergraduate level in Electronic Engineering, Computer Science, Mathematics or a related discipline.

Good 2:2 degrees are considered on an individual basis.

Months of entry


Course content

This programme is designed to train engineers to work 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.

This course will enable you to:

  • Undertake high-level training in programming languages, tools and methods that are 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, live research projects, gaining hands-on experience
  • Work with the most up-to-date technologies - each topic is backed up by our world-leading research centres and groups

You will undertake detailed study in machine learning, both generally and specifically for visual data analytics. You will also study computer vision. You may choose from elective modules in subjects such as computer graphics, data mining or processing, AI, image processing, digital media and social networks.

Our excellent links will allow you to build your network with industry and potential employers and have opportunities to work together on commercial and research projects.

The School also has collaborations, partnerships, industrial placement schemes and public engagement programmes with organisations, including Vodafone, Google, IBM, BT, NASA, BBC and Microsoft.

Qualification, course duration and attendance options

  • MSc
    full time
    12 months
    • Campus-based learningis available for this qualification
    part time
    24 months
    • Campus-based learningis available for this qualification

Course contact details

School of Electronic Engineering and Computer Science
+44 (0)20 7882 7333