Msc machine learning for visual data analytics electronic engineering

Queen Mary University of London
4.5
2 reviews
  • The course is very professionals and the team is supportive. Thanks to them.
    ← | →
  • This course was a great learning experience for me and my amzing tutor Sue made it much more helpful.
    ← | →

Postgraduate

In London

Price on request

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Description

  • Type

    Postgraduate

  • Location

    London

Overview
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.

Facilities

Location

Start date

London
See map
67-69 Lincoln'S Inn Fields, WC2A 3JB

Start date

On request

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Reviews

4.5
  • The course is very professionals and the team is supportive. Thanks to them.
    ← | →
  • This course was a great learning experience for me and my amzing tutor Sue made it much more helpful.
    ← | →
100%
4.9
excellent

Course rating

Recommended

Centre rating

Student

5.0
22/03/2019
About the course: The course is very professionals and the team is supportive. Thanks to them.
Would you recommend this course?: Yes

Student

4.0
22/03/2019
About the course: This course was a great learning experience for me and my amzing tutor Sue made it much more helpful.
Would you recommend this course?: Yes
*All reviews collected by Emagister & iAgora have been verified

This centre's achievements

2019

All courses are up to date

The average rating is higher than 3.7

More than 50 reviews in the last 12 months

This centre has featured on Emagister for 14 years

Subjects

  • Full Time
  • Part Time
  • Engineering
  • Industry
  • Systems
  • Project
  • Image
  • 3D
  • Art
  • 3d training
  • Design
  • Electronic Engineering
  • Options
  • Interpretation

Course programme

Structure

Programme structure

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

Full-time

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

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 Modules

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.

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Msc machine learning for visual data analytics electronic engineering

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