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Masters Degrees (Computer Vision And Machine Learning)

We have 62 Masters Degrees (Computer Vision And Machine Learning)

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This degree provides in-depth training for students interested in a career in industry or in research-oriented institutions focused on image and video analysis, and deep learning. Read more

This degree provides in-depth training for students interested in a career in industry or in research-oriented institutions focused on image and video analysis, and deep learning.

State-of-the-art computer-vision and machine-learning approaches for image and video analysis are covered in the course, as well as low-level image processing methods.

Students also have the chance to substantially expand their programming skills through projects they undertake.

Read about the experience of a previous student on this course, Gianmarco Addari.

Programme structure

This programme is studied full-time over 12 months and part-time from 24 to 60 months. It consists of eight taught modules and a standard project. 

Example module listing

The following modules are indicative, reflecting the information available at the time of publication. Please note that not all modules described are compulsory and may be subject to teaching availability and/or student demand.

Technical characteristics of the pathway

This programme in Computer Vision, Robotics and Machine Learning aims to provide a high-quality advanced training in aspects of computer vision for extracting information from image and video content or enhancing its visual quality using machine learning codes.

Computer vision technology uses sophisticated signal processing and data analysis methods to support access to visual information, whether it is for business, security, personal use or entertainment.

The core modules cover the fundamentals of how to represent image and video information digitally, including processing, filtering and feature extraction techniques.

An important aspect of the programme is the software implementation of such processes. Students will be able to tailor their learning experience through selection of elective modules to suit their career aspirations.

Key to the programme is cross-linking between core methods and systems for image and video analysis applications. The programme has strong links to current research in the Department of Electronic Engineering’s Centre for Vision, Speech and Signal Processing.

Facilities, equipment and support

To support your learning, we hold regular MSc group meetings where any aspect of the programme, technical or non-technical, can be discussed in an informal atmosphere. This allows you to raise any problems that you would like to have addressed and encourages peer-based learning and general group discussion.

We provide computing support with any specialised software required during the programme, for example, Matlab. The Faculty’s student common room is also covered by the University’s open-access wireless network, which makes it a very popular location for individual and group work using laptops and mobile devices.

Specialist experimental and research facilities, for computationally demanding projects or those requiring specialist equipment, are provided by the Centre for Vision, Speech and Signal Processing (CVSSP).

Career prospects

Computer vision specialists are 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 (Google, Microsoft, Apple)
  • Motion capture (Foundry)
  • Media production (BBC, Foundry)
  • Medical Imaging (Siemens)
  • Security and Defence (BAE, EADS, Qinetiq)
  • Robotics (SSTL)

Studying for Msc degree in Computer Vision offers variety, challenge and stimulation. It is not just the introduction to a rewarding career, but also offers an intellectually demanding and exciting opportunity to break through boundaries in research.

Many of the most remarkable advancements in the past 60 years have only been possible through the curiosity and ingenuity of engineers. Our graduates have a consistently strong record of gaining employment with leading companies.

Employers value the skills and experience that enable our graduates to make a positive contribution in their jobs from day one.

Industrial collaborations

We draw on our industry experience to inform and enrich our teaching, bringing theoretical subjects to life. Our industrial collaborations include:

  • Research and technology transfer projects with industrial partners such as the BBC, Foundry, LionHead and BAE
  • A number of our academics offer MSc projects in collaboration with our industrial partners

Research perspectives

This course gives an excellent preparation for continuing onto PhD studies in computer vision related domains.

Global opportunities

We often give our students the opportunity to acquire international experience during their degrees by taking advantage of our exchange agreements with overseas universities.

In addition to the hugely enjoyable and satisfying experience, time spent abroad adds a distinctive element to your CV.



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This MSc teaches advanced analytical and computational skills for success in a data rich world. Read more

This MSc teaches advanced analytical and computational skills for success in a data rich world. Designed to be both mathematically rigorous and relevant, the programme covers fundamental aspects of machine learning and statistics, with potential options in information retrieval, bioinformatics, quantitative finance, artificial intelligence and machine vision.

About this degree

The programme aims to provide graduates with the foundational principles and the practical experience needed by employers in the area of machine learning and statistics. Graduates of this programme will have had the opportunity to develop their skills by tackling problems related to industrial needs or to leading-edge research.

Students undertake modules to the value of 180 credits.

The programme consists of two core modules (30 credits), four to six optional modules (60 to 90 credits), up to two elective modules (up to 30 credits) and a research project (60 credits). Please note that not all combinations of optional modules will be available due to timetabling restrictions.

Core modules

  • Supervised Learning (15 credits)
  • Statistical Modelling and Data Analysis (15 credits)

Optional modules

Students must choose 15 credits from Group One Options. Of the remaining credits, students must choose a minimum of 30 and a maximum of 60 from Group Two, 15 credits from Group Three and a maximum of 30 credits from Electives.

Group One Options (15 credits)

  • Graphical Models (15 credits)
  • Probabilistic and Unsupervised Learning (15 credits)

Group Two Options (30 to 60 credits)

  • Advanced Deep Learning and Reinforcement Learning (15 credits)
  • Advanced Topics in Machine Learning (15 credits)
  • Applied Machine Learning (15 credits)
  • Approximate Inference and Learning in Probabilistic Models (15 credits)
  • Information Retrieval and Data Mining (15 credits)
  • Introduction to Deep Learning (15 credits)
  • Machine Vision (15 credits)
  • Statistical Natural Language Processing (15 credits)

Group Three Options (15 credits)

  • Applied Bayesian Methods (15 credits)
  • Statistical Design of Investigations (15 credits)
  • Statistical Inference (15 credits)

Please note: the availability and delivery of optional modules may vary, depending on your selection.

A list of acceptable elective modules is available on the Departmental page.

Dissertation/report

All MSc students undertake an independent research project, which culminates in a dissertation of 10,000-12,000 words.

Teaching and learning

The programme is delivered through a combination of lectures, discussions, practical sessions and project work. Student performance is assessed through unseen written examinations, coursework, practical application and the project assessment process.

Further information on modules and degree structure is available on the department website: Computational Statistics and Machine Learning MSc

Careers

There is a strong national and international demand for graduates with skills at the interface of traditional statistics and machine learning. Substantial sectors of UK industry, including leading, large companies already make extensive use of computational statistics and machine learning techniques in the course of their business activities. Globally there are a large number of very successful users of this technology, many located in the UK. Areas in which expertise in statistics and machine learning is in particular demand include: finance, banking, insurance, retail, e-commerce, pharmaceuticals, and computer security. Graduates have gone on to further study at, for example, the Universities of Cambridge, Helsinki, Chicago, as well as at UCL. The MSc is also ideal preparation for a PhD, in statistics, machine learning or a related area.

Recent career destinations for this degree

  • Data Scientist, Interpretive
  • Software Engineer, Google
  • Data Scientist, YouGov
  • Research Engineer, DeepMind
  • PhD in Computer Science, UCL

Employability

Scientific experiments and companies now routinely generate vast databases and machine learning and statistical methodologies are core to their analysis. There is a considerable shortfall in the number of qualified graduates in this area internationally. CSML graduates have been in high demand for PhD positions across the sciences. In London there are many companies looking to understand their customers better who have hired our CSML graduates. Similarly graduates now work in companies in, amongst others, Germany, Iceland, France and the US in large-scale data analysis. The finance sector has also hired several graduates recently.

Careers data is taken from the ‘Destinations of Leavers from Higher Education’ survey undertaken by HESA looking at the destinations of UK and EU students in the 2013–2015 graduating cohorts six months after graduation.

Why study this degree at UCL?

The Centre for Computational Statistics and Machine Learning (CSML) is a major European Centre for machine learning having coordinated the PASCAL European Network of Excellence.

Coupled with the internationally renowned Gatsby Computational Neuroscience and the Machine Learning Unit, and UCL Statistical Science, this MSc programme draws on world-class research and teaching talents. The centre has excellent links with world-leading companies in internet technology, finance and related information areas.

The programme is designed to train students in both the practical and theoretical sides of machine learning. A significant grounding in computational statistics is also provided.

Research Excellence Framework (REF)

The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.

The following REF score was awarded to the department: Computer Science

96% rated 4* (‘world-leading’) or 3* (‘internationally excellent’)

Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.



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The Machine Learning MSc at UCL is a truly unique programme and provides an excellent environment to study the subject. It introduces the computational, mathematical and business views of machine learning to those who want to upgrade their expertise and portfolio of skills in this domain. Read more

The Machine Learning MSc at UCL is a truly unique programme and provides an excellent environment to study the subject. It introduces the computational, mathematical and business views of machine learning to those who want to upgrade their expertise and portfolio of skills in this domain.

About this degree

Students develop an understanding of the principles underlying the development and application of new techniques in this area, alongside an awareness of, and ability to analyse the range and scope of algorithms and approaches available, and design, develop and evaluate appropriate algorithms and methods for new problems and applications.

Students undertake modules to the value of 180 credits.

The programme consists of one core module (15 credits), five to seven optional modules (75 to 105 credits), up to two modules (30 credits) from electives, and a research project (60 credits).

Core modules

  • Supervised Learning (15 credits)

Optional modules

Students must choose 15 credits from Option Group One and a minimum of 60 credits from Option Group Two. Students must choose a further 30 credits from either Option Group Two or approved electives.

Option Group One (choose 15 credits)

  • Graphical Models (15 credits)
  • Probabilistic and Unsupervised Learning (15 credits)

Option Group Two (choose 60 to 90 credits)

  • Advanced Deep Learning and Reinforcement Learning (15 credits)
  • Advanced Topics in Machine Learning (15 credits)
  • Affective Computing and Human-Robot Interaction (15 credits)
  • Applied Machine Learning (15 credits)
  • Approximate Inference and Learning in Probabilistic Models (15 credits)
  • Bioinformatics (15 credits)
  • Information Retrieval and Data Mining (15 credits)
  • Introduction to Deep Learning (15 credits)
  • Machine Vision (15 credits)
  • Programming and Mathematical Methods for Machine Learning (15 credits)
  • Statistical Natural Language Programming (15 credits)

Please note: the availability and delivery of optional modules may vary, depending on your selection.

Students may select up to 30 credits from elective modules

A list of acceptable elective modules is available on the departmental website.

Dissertation/report

All MSc students undertake an independent research project which culminates in a dissertation of 10,000-12,000 words in the form of a project report.

Teaching and learning

The programme is delivered through a combination of lectures, seminars, class discussions and project supervision. Student performance is assessed though a combination of unseen written examination, coursework (much of which involves programming and/or data analysis), practical application, and the research project.

Further information on modules and degree structure is available on the department website: Machine Learning MSc

Careers

Graduates from this programme have an excellent employment record. Substantial sectors of UK industry, including leading, large companies already make extensive use of intelligent systems techniques in the course of their business activities, and the UK has a number of very successful developers and suppliers of the technology. Students also benefit from strong corporate and academic connections within the UCL Computer Science alumni network.

Graduates have taken machine learning research degrees in domains as diverse as robotics, music, psychology, and bioinformatics at the Universities of Basel, Cambridge, Edinburgh, Nairobi, Oxford and at UCL. Graduates have also found positions with multinational companies such as BAE Systems and BAE Detica.

Recent career destinations for this degree

  • Computer Vision Engineer, ZVR
  • Data Analyst / Data Scientist, Deloitte Data Analytics Group
  • Programmatic Yield Manager and Data Analyst, eBay
  • Data Scientist, dunnhumby
  • PhD in Computer Science, UCL

Employability

Scientific experiments and companies now routinely generate vast databases and machine learning and statistical methodologies are core to their analysis. There is a considerable shortfall in the number of qualified graduates in this area internationally. Machine Learning graduates have been in high demand for PhD positions across the sciences. In London there are many companies looking to understand their customers better who have hired our graduates. Similarly graduates now work in companies in Germany, Iceland, France and the US, amongst other places, in large-scale data analysis. The finance sector has also hired several graduates recently.

Careers data is taken from the ‘Destinations of Leavers from Higher Education’ survey undertaken by HESA looking at the destinations of UK and EU students in the 2013–2015 graduating cohorts six months after graduation.

Why study this degree at UCL?

UCL Computer Science is recognised as a world leader in teaching and research, and our Master's programmes have some of the highest employment rates and starting salaries.

We take an experimental approach to our subject, enjoy the challenge and opportunity of entrepreneurial partnerships and place a high value on our extensive range of industrial collaborations.

This MSc is one of the few leading Master's programmes entirely dedicated to machine learning. It combines a rigorous theoretical academic framework along with specific knowledge of a variety of application fields to fast-track your commercial career or to prepare for PhD research.

Research Excellence Framework (REF)

The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.

The following REF score was awarded to the department: Computer Science

96% rated 4* (‘world-leading’) or 3* (‘internationally excellent’)

Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.



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There is a high demand from industry worldwide, including from substantial sectors in the UK, for graduates with skills at the interface of traditional statistics and machine learning. Read more

There is a high demand from industry worldwide, including from substantial sectors in the UK, for graduates with skills at the interface of traditional statistics and machine learning. MRes graduates benefit from the department's excellent links in finding employment; this programme is also ideal preparation for a research career.

About this degree

The programme aims to provide graduates with the foundational principles and the practical experience needed by employers in the areas of computational statistics and machine learning (CSML). Students will have the opportunity to develop their skills by tackling problems related to industrial needs or to leading-edge research. They also undertake a nine-month research project which enables the department to more fully assess their research potential.

Students undertake modules to the value of 180 credits.

The programme consists of three core modules (30 credits), three optional modules (45 credits) and a dissertation (105 credits).

Core modules

  • Investigating Research
  • Researcher Professional Development

Optional modules

Student select three modules from the following:

  • Advanced Deep Learning and Reinforcement Learning
  • Advanced Topics in Machine Learning
  • Applied Bayesian Methods
  • Approximate Inference and Learning in Probabilistic Models
  • Graphical Models
  • Information Retrieval and Data Mining
  • Introduction to Deep Learning
  • Introduction to Machine Learning
  • Inverse Problems in Imaging
  • Machine Vision
  • Probabilistic and Unsupervised Learning
  • Selected Topics in Statistics
  • Statistical Computing
  • Statistical Inference
  • Statistical Models and Data Analysis
  • Supervised Learning

Dissertation/report

All students undertake an independent research project which culminates in a substantial dissertation.

Teaching and learning

The programme is delivered through a combination of lectures, tutorials and seminars. Lectures are often supported by laboratory work with assistance from demonstrators. Students liaise with their academic or industrial supervisor to choose a study area of mutual interest for the research project. Performance is assessed by unseen written examinations, coursework and the research dissertation.

Further information on modules and degree structure is available on the department website: Computational Statistics and Machine Learning MRes

Careers

Graduates have gone on to further study at, for example, the Universities of Cambridge, Helsinki, and Chicago, as well as at UCL. Similarly, CSML graduates now work in companies in Germany, Iceland, France and the US in large-scale data analysis. The finance sector is also particularly interested in CSML graduates.

Employability

Scientific experiments and companies now routinely generate vast databases, and machine learning and statistical methodologies are core to their analysis. There is a considerable shortfall in the number of qualified graduates in this area internationally, while in London there are many companies looking to understand their customers better who have hired CSML graduates. Computational statistics and machine learning skills are in particular demand in areas including finance, banking, insurance, retail, e-commerce, pharmaceuticals, and computer security. CSML graduates have obtained PhD positions both in machine learning and related large-scale data analysis, and across the sciences.

Why study this degree at UCL?

The Centre for Computational Statistics and Machine Learning (CSML) is a major European Centre for machine learning, having co-ordinated the PASCAL European Network of Excellence which represents the largest network of machine learning researchers in Europe.

UCL Computer Science graduates are particularly valued by the world’s leading organisations in internet technology, finance, and related information areas, as a result of the department’s strong international reputation and ideal location close to the City of London.

Research Excellence Framework (REF)

The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.

The following REF score was awarded to the department: Computer Science

96% rated 4* (‘world-leading’) or 3* (‘internationally excellent’)

Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.



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Data Science brings together computational and statistical skills and machine learning for data-driven problem solving. Read more

Data Science brings together computational and statistical skills and machine learning for data-driven problem solving. This rapidly expanding area includes deep learning, large-scale data analysis and has applications in e-commerce, search/information retrieval, natural language modelling, finance, bioinformatics and related areas in artificial intelligence.

About this degree

The programme comprises core machine learning methodology and an introduction to statistical science, combined with a set of more specialised and advanced options covering computing and statistical modelling. Projects are offered both within UCL Computer Science and from a range of industry partners.

Students undertake modules to the value of 180 credits.

The programme consists of three compulsory modules (45 credits), four optional modules (75 credits) and a dissertation/report (60 credits).

Core modules

  • Applied Machine Learning (15 credits)
  • Introduction to Machine Learning (15 credits)
  • Introduction to Statistical Data Science (15 credits)

Optional modules

Students must choose 30 credits from Group One options. For the remaining 45 credits, students may choose up to 30 credits from Group Two options or up to 45 credits from Electives.

Group One Options (30 credits)

  • Advanced Deep Learning and Reinforcement Learning (15 credits)
  • Birkbeck College: Cloud Computing (15 credits)
  • Information Retrieval and Data Mining (15 credits)
  • Introduction to Deep Learning (15 credits)
  • Machine Vision (15 credits)
  • Statistical Natural Language Processing (15 credits)
  • Web Economics (15 credits)

Group Two Options (up to 30 credits)

  • Applied Bayesian Methods (15 credits)
  • Decision and Risk (15 credits)
  • Forecasting (15 credits)
  • Statistical Design of Investigations (15 credits)

Electives (up to 45 credits)

  • Affective Computing and Human-Robot Interaction (15 credits)
  • Bioinformatics (15 credits)
  • Computational Modelling for Biomedical Imaging (15 credits)
  • Graphical Models (15 credits)
  • Stochastic Systems (15 credits)
  • Supervised Learning (15 credits)

Please note: the availability and delivery of modules may vary, based on your selected options.

A list of acceptable elective modules is available on the Departmental page.

Dissertation/report

All students undertake an independent research project which culminates in a dissertation of 10,000-12,000 words.

Teaching and learning

The programme is delivered though a combination of lectures, seminars, class discussions and project supervision. Student performance is assessed through a combination of unseen written examination, coursework (much of which involves programming and/or data analysis), practical application, and the research project.

Further information on modules and degree structure is available on the department website: Data Science and Machine Learning MSc

Careers

Data science professionals are increasingly sought after as the integration of statistical and computational analytical tools becomes more essential to organisations. This is a very new degree and information on graduate destinations is not currently available. However, MSc graduates from across the department frequently find roles with major tech and finance companies including:

  • Google Deepmind
  • Microsoft Research
  • Dunnhumby
  • Index Ventures
  • Cisco
  • Deutsche Bank
  • IBM
  • Morgan Stanley

Employability

Students gain a thorough understanding of the fundamentals required from the best practitioners, and the programme's broad base enables data scientists to adapt to rapidly evolving goals.

Why study this degree at UCL?

UCL received the highest percentage (96%) for quality of research in Computer Science and Informatics in the UK's most recent Research Excellence Framework (REF2014).

UCL Computer Science staff have research interests ranging from foundational machine learning and large-scale data analysis to commercial aspect of business intelligence. Our extensive links to companies provide students with opportunities to carry out the research project with an industry partner.

The department also enjoys strong collaborative relationships across UCL; exposure to interdisciplinary research spanning UCL Computer Science and UCL Statistical Science will provide students with a broad perspective of the field. UCL is home to regular machine learning masterclasses and big data seminars.

Research Excellence Framework (REF)

The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.

The following REF score was awarded to the department: Computer Science

96% rated 4* (‘world-leading’) or 3* (‘internationally excellent’)

Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.



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The fields of graphics, vision and imaging increasingly rely on one another. Read more

The fields of graphics, vision and imaging increasingly rely on one another. This unique and timely MSc provides training in computer graphics, geometry processing, virtual reality, machine vision and imaging technology from world-leading experts, enabling students to specialise in any of these areas and gain a grounding in the others.

About this degree

Graduates will understand the basic mathematical principles underlying the development and application of new techniques in computer graphics and computer vision and will be aware of the range of algorithms and approaches available, and be able to design, develop and evaluate algorithms and methods for new problems, emerging technologies and applications.

Students undertake modules to the value of 180 credits.

The programme consists of four core modules (60 credits), four optional modules (60 credits) and a research project (60 credits).

Core modules

  • Computer Graphics (15 credits)
  • Image Processing (15 credits)
  • Mathematical Methods, Algorithmics and Implementations (15 credits)
  • Research Methods and Reading (15 credits)

Optional modules

Students must choose a minimum of 15 and a maximum of 30 credits from Group One options. Students must choose a minimum of 30 and a maximum of 45 credits from Group Two options.

Group One Options (15 to 30 credits)

  • Machine Vision (15 credits)
  • Virtual Environments (15 credits)

Group Two Options (30 to 45 credits)

  • Acquisition and Processing of 3D Geometry (15 credits)
  • Computational Modelling for Biomedical Imaging (15 credits)
  • Computational Photography and Capture (15 credits)
  • Geometry of Images (15 credits)
  • Graphical Models (15 credits)
  • Information Processing in Medical Imaging (15 credits)
  • Introduction to Machine Learning (15 credits)
  • Inverse Problems in Imaging (15 credits)
  • Numerical Optimisation (15 credits)
  • Robotic Sensing, Manipulation and Interaction (15 credits)
  • Robotic Vision and Navigation (15 credits)

Please note: the availability and delivery of optional modules may vary, depending on your selection.

Dissertation/report

All students undertake an independent research project related to a problem of industrial interest or on a topic near the leading edge of research, which culminates in a 60–80 page dissertation.

Teaching and learning

The programme is delivered through a combination of lectures and tutorials. Lectures are often supported by laboratory work with help from demonstrators. Student performance is assessed by unseen written examinations, coursework and a substantial individual project.

Further information on modules and degree structure is available on the department website: Computer Graphics, Vision and Imaging MSc

Careers

Graduates are ready for employment in a wide range of high-technology companies and will be able to contribute to maintaining and enhancing the UK's position in these important and expanding areas. The MSc provides graduates with the up-to-date technical skills required to support a wealth of research and development opportunities in broad areas of computer science and engineering, such as multimedia applications, medicine, architecture, film animation and computer games. Our market research shows that the leading companies in these areas demand the deep technical knowledge that this programme provides. Graduates have found positions at global companies such as Disney, Sony and Siemens. Others have gone on to PhD programmes at leading universities worldwide.

Recent career destinations for this degree

  • Business Analyst, Adobe
  • Software Engineer, FactSet Research Systems
  • MRes in Engineering, Imperial College London
  • Software Engineer, Sengtian Software
  • PhD in Computer Graphics, UCL

Employability

UCL received the highest percentage (96%) for quality of research in Computer Science and Informatics in the UK's most recent Research Excellence Framework (REF2014).

Our graduates have some of the highest employment rates of any university in the UK. This degree programme also provides a foundation for further PhD study or industrial research.

Careers data is taken from the ‘Destinations of Leavers from Higher Education’ survey undertaken by HESA looking at the destinations of UK and EU students in the 2013–2015 graduating cohorts six months after graduation.

Why study this degree at UCL?

UCL Computer Science contains some of the world's leading researchers in computer graphics, geometry processing, computer vision and virtual environments.

Research activities include geometric acquisition and 3D fabrication, real-time photo-realistic rendering, mixed and augmented reality, face recognition, content-based image-database search, video-texture modelling, depth perception in stereo vision, colour imaging for industrial inspection, mapping brain function and connectivity and tracking for SLAM (simultaneous localisation and mapping).

Research Excellence Framework (REF)

The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.

The following REF score was awarded to the department: Computer Science

96% rated 4* (‘world-leading’) or 3* (‘internationally excellent’)

Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.



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Learn how to research, design and develop machine learning and autonomous systems technologies. You’ll be prepared for a wide range of careers in industry. Read more

Learn how to research, design and develop machine learning and autonomous systems technologies. You’ll be prepared for a wide range of careers in industry.

Intelligent and autonomous systems are increasingly important in all areas of human life and activity from medicine and space exploration to agriculture and entertainment.

Understanding and developing autonomous systems involves a range of skills and knowledge including designing interactive systems with both human and machine elements, and modelling and building systems that can sense and learn.

Machine learning is at the heart of autonomous and intelligent systems, including computer vision and robotics. It also underpins the recent developments in data analytics across many fields.

You will learn to use new knowledge to solve complex machine learning and autonomous systems problems. You’ll develop a range of skills including the theory of machine learning, artificial intelligence, autonomous systems design and engineering, and the implications for humans of interacting more and more with intelligent and autonomous systems.

You will be taught by academics from the Department of Computer Science with expertise in machine learning, autonomous systems, artificial intelligence and human-computer interaction. This course has been designed in collaboration with the Department of Electronic and Electrical Engineering who offer expertise in robotics.

You will study in a research-led department with a supportive postgraduate community. You’ll learn in our bespoke computer laboratory and be exposed to the latest ideas and technology. The department has strong links to industry both nationally and internationally.

With machine learning and autonomous systems forming an essential part of a number of key industries, our MSc graduates will be highly sought after by employers.

You’ll gain the knowledge and transferable skills for a career in a wide range of industries, or for further study at PhD level. Graduates from the department have gone on to work in a wide variety of sectors, including IT consultancy, software development, banking and education.

Visit the website.



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Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Computer Science. Read more

Visit our website for more information on fees, scholarships, postgraduate loans and other funding options to study Computer Science: Informatique at Swansea University - 'Welsh University of the Year 2017' (Times and Sunday Times Good University Guide 2017).

The MSc in Computer Science: Informatique is a Dual Degree scheme between Swansea University and Université Grenoble Alpes for computer science.

The MSc in Computer Science: Informatique Grenoble dual degree scheme is a two year programme that provides students with an opportunity to study in both Swansea, UK and Grenoble, France. One year of the Computer Science: Informatique programme students study at Swansea University and the second year of the programme students study at Université Grenoble Alpes. Upon successful completion of the programme, students will receive an M.Sc. in Advanced Computer Science from Swansea University and a Master from Université Grenoble Alpes.

Key Features of Computer Science: Informatique MSc

- We are top in the UK for career prospects [Guardian University Guide 2018]

- 5th in the UK overall [Guardian University Guide 2018]7th in the UK for student satisfaction with 98% [National Student Survey 2016]

- We are in the UK Top 10 for teaching quality [Times & Sunday Times University Guide 2017]

- 12th in the UK overall and Top in Wales [Times & Sunday Times University Guide 2017]

- 92% in graduate employment or further study six months after leaving University [HESA data 2014/15]

- UK TOP 20 for Research Excellence [Research Excellence Framework 2014]

- Our Project Fair allows students to present their work to local industry

- Strong links with industry

- £31m Computational Foundry for computer and mathematical sciences will provide the most up-to-date and high quality teaching facilities featuring world-leading experimental set-ups, devices and prototypes to accelerate innovation and ensure students will be ready for exciting and successful careers. (From September 2018)

- Top University in Wales [Times & Sunday Times University Guide 2017]

Modules of Computer Science: Informatique MSc

Modules on the MSc in Computer Science: Informatique may include:

Critical Systems; IT-Security: Theory and Practice; Visual Analytics; Data Science Research Methods and Seminars; Big Data and Data Mining; Data Visualization; Human Computer Interaction; Big Data and Machine Learning; Web Application Development; High Performance Computing in C/C++; Software Testing; Graphics Processor Programming; Embedded System Design; Mathematical Skills for Data Scientists; Logic in Computer Science; Computer Vision and Pattern Recognition; High-Performance Computing in C/C++; Hardware and Devices; Modelling and Verification Techniques; Operating Systems and Architectures.

Facilities

The Department of Computer Science is well equipped for teaching, and is continually upgrading its laboratories to ensure equipment is up-to-date – equipment is never more than three years old, and rarely more than two. Currently, Computer Science students use three fully networked laboratories: one, running Windows; another running Linux; and a project laboratory, containing specialised equipment. These laboratories support a wide range of software, including the programming languages Java, C# and the .net framework, C, C++, Haskell and Prolog among many; integrated programme development environments such as Visual Studio and Netbeans; the widely-used Microsoft Office package; web access tools; and many special purpose software tools including graphical rendering and image manipulation tools; expert system production tools; concurrent system modelling tools; World Wide Web authoring tools; and databases.

As part of our expansion, we are building the Computational Foundry on our Bay Campus for computer and mathematical sciences. This development is exciting news for Swansea Mathematics who are part of the vibrant and growing community of world-class research leaders drawn from computer and mathematical sciences.

Careers

All Computer Science courses will provide you the transferable skills and knowledge to help you take advantage of the excellent employment and career development prospects in an ever growing and changing computing and ICT industry.

94% of our Postgraduate Taught Computer Science Graduates were in professional level work or study [DLHE 14/15].

Some example job titles include:

Software Engineer: Motorola Solutions

Change Coordinator: Logica

Software Developer/Engineer: NS Technology

Workflow Developer: Irwin Mitchell

IT Developer: Crimsan Consultants

Consultant: Crimsan Consultants

Programmer: Evil Twin Artworks

Web Developer & Web Support: VSI Thinking

Software Developer: Wireless Innovations

Associate Business Application Analyst: CDC Software

Software Developer: OpenBet Technologies

Technical Support Consultant: Alterian

Programming: Rock It

Software Developer: BMJ Group

Research

The results of the Research Excellence Framework (REF) 2014 show that Swansea Computer Science ranked 11th in the UK for percentage of world-leading research, and 1st in Wales for research excellence. 40% of our submitted research assessed as world-leading quality (4*).



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Developed by the Bristol Robotics Laboratory, this Masters gives students unique exposure to world-leading robotics research, real-life automation and computer vision projects, and the opportunity for placements in UK companies to work on topical industry problems. Read more
Developed by the Bristol Robotics Laboratory, this Masters gives students unique exposure to world-leading robotics research, real-life automation and computer vision projects, and the opportunity for placements in UK companies to work on topical industry problems.

The last 20 years have seen a phenomenal growth in the development and application of computer and machine vision technology. With increasingly complex applications across diverse areas, including manufacturing, security and medicine, there is a growing need for professionals who can evaluate, design and implement technically appropriate and economically viable automation systems for enhancing quality and productivity.

The MSc in Automation and Computer Vision at UWE Bristol is one of the very few postgraduate courses that brings together both of these disciplines into one industry-focused, research-informed Masters.

Key benefits

Some students may be able to do an industry placement as part of their dissertation. Projects will be focused on real problems companies are working on. Those that don't go down the industry route will work at UWE Bristol on a topical research problem.

Course detail

The course provides a unique combination of these two overlapping disciplines, with a strong emphasis on robotics hardware for solving 'real-world' problems. You will develop both the technical knowledge and the business skills needed to introduce advanced automation and machine vision techniques in the workplace.

You will also benefit from the University's close links with industry, with guest lectures on many modules and the chance to work on real-life automation and computer vision projects.

Modules

• Automation and Control (30 credits)
• Machine Vision (30 credits)
• Managing finance (15 credits)
• Project management (15 credits)
• Industrial applications (15 credits)
• Industrial case studies (15 credits)

You will also work on an individual project (60 credits), which forms a major part of the course and gives you the chance to work on real-world research or industry projects

Format

Alongside the strong industry-focus of the course, you will have the opportunity to be part of, and work on, projects in the world-leading Bristol Robotics Laboratory, which brings together influential researchers in service robotics, autonomous systems and bio-engineering.

For those already working, we offer this course as a work-based learning course, as well as a standard full or part-time Masters. Employees of relevant industries can attend part of the course to supplement their existing skills or to be assessed on their current skills and knowledge of these highly topical subject areas.

Assessment

We will make use of a range of types of assessment on the course, including written exams, oral assessments and presentations, reports and project work and written assignments.

Careers / Further study

The course is a good grounding for wider careers in engineering, science, information technology, management and medical imaging. For those wishing to pursue further study, the course is also good preparation for a career in academia or research in fields such as computer vision, robotics, medical imaging, or more general engineering, science and information technology.

How to apply

Information on applications can be found at the following link: http://www1.uwe.ac.uk/study/applyingtouwebristol/postgraduateapplications.aspx

Funding

- New Postgraduate Master's loans for 2016/17 academic year –

The government are introducing a master’s loan scheme, whereby master’s students under 60 can access a loan of up to £10,000 as a contribution towards the cost of their study. This is part of the government’s long-term commitment to enhance support for postgraduate study.

Scholarships and other sources of funding are also available.

More information can be found here: http://www1.uwe.ac.uk/students/feesandfunding/fundingandscholarships/postgraduatefunding.aspx

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Our modern world is witnessing a growth of online data in a variety of forms, including web documents, blogs, social networks, digital libraries and medical records. Read more
Our modern world is witnessing a growth of online data in a variety of forms, including web documents, blogs, social networks, digital libraries and medical records. Much of this data contains valuable information, such as emerging opinions in social networks, search trends from search engines, consumer purchase behaviour, and patterns that emerge from these huge data sources.

The sheer volume of this information means that traditional stand-alone applications are no longer suitable to process and analyse this data. Our course equips you with the knowledge to contribute to this rapidly emerging area.

We give you hands-on experience with various types of large-scale data and information handling, and start by providing you with a solid understanding of the underlying technologies, in particular cloud computing and high-performance computing. You explore areas including:
-Mobile and social application programming
-Human-computer interaction
-Computer vision
-Computer networking
-Computer security

You also obtain practical knowledge of processing textual data on a large scale in order to turn this data into meaningful information, and have the chance to work on projects that are derived from actual industry needs proposed by our industrial partners.

We are ranked Top 10 in the UK in the 2015 Academic Ranking of World Universities, with more than two-thirds of our research rated ‘world-leading’ or ‘internationally excellent (REF 2014).

This degree is accredited by the Institution of Engineering and Technology (IET).This accreditation is increasingly sought by employers, and provides the first stage towards eventual professional registration as a Chartered Engineer (CEng).

Our expert staff

Today’s computer scientists are creative people who are focused and committed, yet restless and experimental. We are home to many of the world’s top scientists, and our staff are driven by creativity and imagination as well as technical excellence. We are conducting world-leading research in areas such as evolutionary computation, brain-computer interfacing, intelligent inhabited environments and financial forecasting.

Specialist staff working on data analytics include:
-Dr Luca Citi – machine learning, learning from biological signals and data (EEG, etc)
-Dr Adrian Clark – automatic construction of vision systems using machine learning and evaluation of algorithms, data visualisation and augmented reality
-Professor Maria Fasli – analysis of structured/unstructured data, machine learning, adaptation, semantic information extraction, ontologies, data exploration, recommendation technologies
-Professor John Gan – machine learning for data modelling and analysis, dimensionality reduction and feature selection in high-dimensional data space
-Dr Udo Kruschwitz – natural language processing, analysis textual/unstructured data, information retrieval
-Professor Massimo Poesio – cognitive science of language, text mining, computational linguistics
-Professor Edward Tsang – applied AI, constraint satisfaction, computational finance and economics, agent-based simulations

Specialist facilities

We are one of the largest and best resourced computer science and electronic engineering schools in the UK. Our work is supported by extensive networked computer facilities and software aids, together with a wide range of test and instrumentation equipment.
-We have six laboratories that are exclusively for computer science and electronic engineering students. Three are open 24/7, and you have free access to the labs except when there is a scheduled practical class in progress
-All computers run either Windows 7 or are dual boot with Linux
-Software includes Java, Prolog, C++, Perl, Mysql, Matlab, DB2, Microsoft Office, Visual Studio, and Project
-Students have access to CAD tools and simulators for chip design (Xilinx) and computer networks (OPNET)
-We also have specialist facilities for research into areas including non-invasive brain-computer interfaces, intelligent environments, robotics, optoelectronics, video, RF and MW, printed circuit milling, and semiconductors

Your future

Demand for skilled graduates in the areas of big data and data science is growing rapidly in both the public and private sector, and there is a predicted shortage of data scientists with the skills to understand and make commercial decisions based on the analysis of big data.

Our recent graduates have progressed to a variety of senior positions in industry and academia. Some of the companies and organisations where our former graduates are now employed include:
-Electronic Data Systems
-Pfizer Pharmaceuticals
-Bank of Mexico
-Visa International
-Hyperknowledge (Cambridge)
-Hellenic Air Force
-ICSS (Beijing)
-United Microelectronic Corporation (Taiwan)

We also work with the university’s Employability and Careers Centre to help you find out about further work experience, internships, placements, and voluntary opportunities.

Example structure

Postgraduate study is the chance to take your education to the next level. The combination of compulsory and optional modules means our courses help you develop extensive knowledge in your chosen discipline, whilst providing plenty of freedom to pursue your own interests. Our research-led teaching is continually evolving to address the latest challenges and breakthroughs in the field, therefore to ensure your course is as relevant and up-to-date as possible your core module structure may be subject to change.

Big Data and Text Analytics - MSc
-MSc Project and Dissertation
-Information Retrieval
-Cloud Technologies and Systems (optional)
-Group Project
-High Performance Computing
-Machine Learning and Data Mining
-Natural Language Engineering
-Professional Practice and Research Methodology
-Text Analytics
-Advanced Web Technologies (optional)
-Data Science and Decision Making (optional)
-Big-Data for Computational Finance (optional)
-Computer Security (optional)
-Computer Vision (optional)
-Creating and Growing a New Business Venture (optional)
-Mobile & Social Application Programming (optional)

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Data science is an emerging new area of science. With City’s MSc in Data Science you can develop the skills and knowledge to analyse data in many forms and communicate insights. Read more
Data science is an emerging new area of science. With City’s MSc in Data Science you can develop the skills and knowledge to analyse data in many forms and communicate insights.

Who is it for?

This programme is for students who have a numerate first degree or can demonstrate numerate skills. Students are often at the early stages of their careers in diverse professions including economics, statistics and computer science.

Students will have a curiosity about data, and will want to learn new techniques to boost their career and be part of exciting current industry developments. The MSc in Data Science includes some complex programming tasks because of the applied nature of the course, so many students have a mathematics or statistics background and enjoy working with algorithms.

Objectives

The demand for data scientists in the UK has grown more than ten-fold in the past five years *. The amount of data in the world is growing exponentially. From analysing tyre performance to detecting problem gamblers, wherever data exists, there are opportunities to apply it.

City’s MSc Data Science programme covers the intersection of computer science and statistics, machine learning and practical applications. We explore areas such as visualisation because we believe that data science is about generating insight into data as well as its communication in practice.

The programme focuses on machine learning as the most exciting technology for data and we have learned from our own graduates that this is of high value when it comes to employment within the field. At City, we have excellent expertise in machine learning and the facilities students need to learn the technical aspects of data analysis. We also have a world-leading centre for data visualisation, where students get exposed to the latest developments on presenting and communicating their results – a highly sought after skill.

Placements

There is the opportunity to do an internship as part of the programme. The final project, which is normally three months for a full-time student, can be extended to six months if you want to study within a specific organisation. When it comes to the big data and data science area, we have established relationships with organisations including the BBC, Microsoft and The British Library so you can be confident that with City, your access to professional experience is unparalleled. One recent student undertook an internship with Google and has since secured a job within the company.

Academic facilities

The School's computer science laboratories are equipped with the latest up-to-date hardware and software. From Oracle’s leading commercial object-relational database server to PCs with state-of-the-art NVidia GPUs for computer graphics, you will have access to an array of tools to support your learning.

The MSc Data Science programme offers two (three by mid 2016) dedicated computer servers for the Big Data module, which you can also use for your final project to analyse large data sets. We give you the opportunity to undertake training in MATLAB, the most popular numerical and technical programming environment, while you study.

Scholarships

A scholarship for the full fees of the MSc will be offered to an outstanding applicant. The scholarship is available to UK/EU and overseas students, studying full-time. To be considered for the scholarship, please include with your full application a one-page essay with your answer to the question:

'What are the challenges that Data Science faces and how would you address those challenges?'

The submission deadline for anyone wishing to be considered for the scholarship is: 1 MAY 2017

Teaching and learning

The teaching and learning methods we use mean that students’ specialist knowledge and autonomy increase as they progress through each module. Active researchers guide your progress in the areas of machine learning, data visualization, and high-performance computing, which culminates with an individual project. This is an original piece of research conducted with academic supervision, but largely independently and, where appropriate, in collaboration with industrial partners.

Taught modules are delivered through a series of 20 hours of lectures and 10 hours of tutorials/laboratory sessions. Lectures are normally used to:
-Present and exemplify the concepts underpinning a particular subject.
-Highlight the most significant aspects of the syllabus.
-Indicate additional topics and resources for private study.

Tutorials help you develop the skills to apply the concepts we have covered in the lectures. We normally achieve this through practical problem solving contexts.

Laboratory sessions give you the opportunity to apply concepts and techniques using state-of-the-art software, environments and development tools.

In addition to lectures, laboratory sessions and tutorial support, you also have access to a personal tutor. This is an academic member of staff from whom you can gain learning support throughout your degree. In addition, City’s online learning environment Moodle contains resources for each of the modules from lecture notes and lab materials, to coursework feedback, model answers, and an interactive discussion forum.

We expect you to study independently and complete coursework for each module. This should amount to approximately 120 hours per module if you are studying full time. Each module is assessed through a combination of written examination and coursework, where you will need to answer theoretical and practical questions to demonstrate that you can analyse and apply data science methods and techniques.

The individual project is a substantial task. It is your opportunity to develop a research-related topic under the supervision of an academic member of staff. This is the moment when you can apply what you have learnt to solve a real-world problem using large datasets from industry, academia or government and use your knowledge of collecting and processing real data, designing and implementing big data methods and applying and evaluating data analysis, visualisation and prediction techniques. At the end of the project you submit a substantial MSc project report, which becomes the mode of assessment for this part of the programme.

Course content

Data science is the area of study concerned with the extraction of insight from large collections of data.

The course covers the study, integration and application of advanced methods and techniques from:
-Data analysis and machine learning
-Data visualisation and visual analytics
-High-performance, parallel and distributed computing
-Knowledge representation and reasoning
-Neural computation
-Signal processing
-Data management and information retrieval.

It gives you the opportunity to specialise so, once you graduate, you can apply data science to any sector from health to retail. By engaging with researchers and industrial partners during the programme, you can develop your knowledge and skills within a real-world context in each of the above areas.

Core modules
-Principles of data science (15 credits)
-Machine learning (15 credits)
-Big Data (15 credits)
-Neural computing (15 credits)
-Visual analytics (15 credits)
-Research methods and professional issues (15 credits)

Elective modules
-Advanced programming: concurrency (15 credits)
-Readings in computer science (15 credits)
-Advanced databases (15 credits)
-Information retrieval (15 credits)
-Data visualisation (15 credits)
-Digital signal processing and audio programming (15 credits)
-Cloud computing (15 credits)
-Computer vision (15 credits)
-Software agents (15 credits)

Individual project - (60 credits)

Career prospects

From health to retail, and from the IT industry to government, the Data Science MSc will prepare you for a successful career as a data scientist. You will graduate with specialist skills in data acquisition, information extraction, aggregation and representation, data analysis, knowledge extraction and explanation, which are in high demand.

City's unique internships, our emphasis on machine learning and visual analytics, together with our links with the industry and Tech City, should help you gain employment as a specialist in data analysis and visualization. Graduates starting a new business can benefit from City's London City Incubator and City's links with Tech City, providing support for start-up businesses.

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Data Science brings together computational and statistical skills for data-driven problem solving. Read more
Data Science brings together computational and statistical skills for data-driven problem solving. This rapidly expanding area includes machine learning, deep learning, large-scale data analysis and has applications in e-commerce, search/information retrieval, natural language modelling, finance, bioinformatics and related areas in artificial intelligence.

Degree information

The programme comprises core machine learning methodology and an introduction to statistical science, combined with a set of more specialised and advanced options covering computing and statistical modelling. Projects are offered both within UCL Computer Science and from a wide range of industry partners.

Students undertake modules to the value of 180 credits.

The programme consists of three compulsory modules (45 credits), five optional modules (75credits) and a dissertation/report (60 credits).

Core modules
-Applied Machine Learning
-Introduction to Supervised Learning
-Introduction to Statistical Data Science

Optional modules - students choose a minimum of 30 credits and a maximum of 60 credits from the following optional modules:
-Cloud Computing (Birkbeck)
-Machine Vision
-Information Retrieval & Data Mining
-Statistical Natural Language Processing
-Web Economics

Students choose a minimum of 0 credits and a maximum of 30 credits from these optional Statistics modules:
-Statistical Design of Investigations
-Applied Bayesian Methods
-Decision & Risk

Students choose a minimum of 15 credits and a maximum of 15 credits from these elective modules:
-Supervised Learning
-Graphical Models
-Bioinformatics
-Affective Computing and Human-Robot Interaction
-Computational Modelling for Biomedical Imaging
-Stochastic Systems
-Forecasting

Dissertation/report
All students undertake an independent research project which culminates in a dissertation of 10,000-12,000 words.

Teaching and learning
The programme is delivered though a combination of lectures, seminars, class discussions and project supervision. Student performance is assessed through a combination of unseen written examination, coursework (much of which involves programming and/or data analysis), practical application, and the research project.

Careers

Data science professionals are increasingly sought after as the integration of statistical and computational analytical tools becomes more essential to organisations. A thorough understanding of the fundamentals required from the best practitioners, and this programme's broad base, assists data scientists to adapt to rapidly evolving goals. This is a new degree and information on graduate destinations is not currently available. However, MSc graduates from across the department frequently find roles with major tech and finance companies including:
-Google Deepmind
-Microsoft Research
-Dunnhumby
-Index Ventures
-Last.fm
-Cisco
-Deutsche Bank
-IBM
-Morgan Stanley

Why study this degree at UCL?

The 2014 Research Excellence Framework ranked UCL first in the UK for computer science. 61% of its research work is rated as world-leading and 96% as internationally excellent.

UCL Computer Science staff have research interests ranging from foundational machine learning and large-scale data analysis to commercial aspect of business intelligence. Our extensive links to companies provide students with opportunities to carry out the research project with an industry partner.

The department also enjoys strong collaborative relationships across UCL; and exposure to interdisciplinary research spanning UCL Computer Science and UCl Statistical Science will provide students with a broad perspective of the field. UCL is home to regular machine learning masterclasses and big data seminars.

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Application period/deadline. November 1, 2017 - January 24, 2018. Research-oriented degree provides an exciting opportunity to study in a leading-edge research environment. Read more

Application period/deadline: November 1, 2017 - January 24, 2018

• Research-oriented degree provides an exciting opportunity to study in a leading-edge research environment

• The studies combine both theoretical and practical approach

• Specializations in Applied Computing, Artificial Intelligence, and Computer Egineering

The International Master’s Degree Programme in Computer Science and Engineering (CSE) is a two-year research-oriented programme concentrating on intelligent digital solutions to real world problems. During the past decades, Computer Science and Engineering has had a significant impact into our daily lives. The development continues and soon computers will not be used as separate devices anymore. Instead they will blend into our living environments and offer us rich sets of services through natural and intuitive user interfaces. The graduates from Computer Science and Engineering will play a key role in this development.

The two-year programme has three specialisation options:

• Applied Computing

• Artificial Intelligence

• Computer Engineering

Applied Computing focuses on the next generation of interactive systems that place humans at the focus of the technological development. Adopting a multidisciplinary real-world approach, students have to spend a substantial amount of time working in group projects to develop a variety of systems ranging from interactive online services to games and mobile applications, with a strong focus on innovation and design.

Artificial Intelligence focuses in various fields of AI, such as machine learning, machine vision, and data mining. This specialisation provides students with a solid theoretical understanding and practical skills on processing and analyzing digital data and the ability to create intelligent solutions to real world problems with modern AI techniques.

Computer Engineering focuses on both hardware and software aspects of computing with emphasis on embedded system development. In this specialisation, students also study signal processing and its applications, and work with projects on modern signal processors and embedded computers. The specialisation gives the students a good basis to work with Internet of Things (IoT) applications.

In addition to the core specialization options, students can take optional courses to widen their specialization expertise into:

• Biomedical signal analysis

• Machine learning

• Machine vision

• Signal processing

• Embedded systems

• Ubiquitous computing

This Master’s programme is provided by the Faculty of Information Technology and Electrical Engineering, and students are strongly encouraged to work closely with research groups in the faculty that are international leaders in their fields. The Center for Machine Vision and Signal Analysis (CMVS) is renowned world-wide for its 35 years of expertise in computer vision research. The Center for Ubiquitous Computing (UBICOMP) has created a unique research environment for Ubiquitous Computing including multitouch wall-sized displays, smartphone sensing middleware and sensor networks. Biomimetics and Intelligent Systems Group (BISG) is a fusion of expertise from the fields of computer science and biology. During the studies the research groups provide students trainee and master’s thesis positions, with the possibility to continue as a doctoral student, and even as a post-doctoral researcher.

The programme will provide the graduates with sufficient skills to work in a wide variety of positions offered by research institutes and companies mainly operating in the field of information and communications technology (ICT). The graduates are most likely to be employed in research and development related positions, but also management positions and entrepreneurship fit into the profile.

Possible titles include:

• Research Scientist

• Software Engineer

• System Designer

• Project Manager

• Specialist

Students applying for the programme must possess an applicable B.Sc. degree in computer science, electrical engineering or relevant fields such as physics or applied mathematics.

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Focus on computer science. The Master of Information Sciences will give you in-depth knowledge and expertise in computer science. Read more

Focus on computer science

The Master of Information Sciences will give you in-depth knowledge and expertise in computer science.

Find out more about the Master of Information Sciences parent structure.

Massey University’s Master of Information Sciences (Computer Science) will give you the knowledge and skills you need to gain a more in-depth understanding of computer science and to advance your career.You will learn how to apply knowledge of ICT technologies and/or management with both a broad world-view and at a specialty level. If you wish to have a broader-based qualification the Master of Science (Computer Science) may be suitable for you.

Flexibility of focus

Within computer science you can make the most of our lecturer’s research expertise to focus on topics that interest you such as:

  • Massey Auckland: artificial intelligence, compilers, computer graphics, computer vision, parallel and distributed computing, operating systems and advanced computer systems.
  • Massey Palmerston North: empirical software engineering, program verification, human-computer interaction, technology - support learning, artificial intelligence, smart environment, machine learning, scientific computing, health informatics and graph algorithms.

In demand

A report by Absolute IT showed that IT employers are seeking increasing numbers of staff. In Auckland alone 75% of IT employers are planning to recruit additional staff and contractors in 2016. The majority of the hiring is taking place because of increased demand and new projects.

The research showed that high demand areas are now software development, business analysis, project management and data/database. These are the key areas of information sciences you can study at Massey.

Giving you relevant work experience

A professional practice project is a major part of this masters. You will have the opportunity to lead real projects for real companies on real issues that they wish to solve. This experience can directly lead to roles and add substantial value to your resume when you are seeking employment.

Complete in 1.5 years

Massey University’s Master of Information Sciences is a 180 credit master qualification offering a rigorous programme of learning that can be achieved in one and a half years full-time study.

Giving you relevant work experience

A professional practice project is a major part of this master’s. You will have the opportunity to lead real projects for real companies on real issues that they wish to solve. This experience can directly lead to roles and add substantial value to your resume when you are seeking employment.

Research-led learning

Massey teaching is research-led and our academics are ranked among the best in the world. Our teaching staff are widely published and are in demand as keynote speakers on topical global issues. International visitors regularly seek out visits to our science facilities to learn more about what we do.

Accessible lecturers

We work to help you succeed. Massey University offers smaller classes and more personalised learning, giving you greater access to lecturers and the help you need to succeed and thrive during your master’s study.

Relevant learning

Your lecturers and supervisors at Massey have strong connections with the industry. We work to ensure that our teaching fits with the changing environment, which means you will emerge with a relevant qualification valued by potential employers.

Why postgraduate study?

Postgraduate study is hard work but hugely rewarding and empowering. The Master of Information Sciences will push you to produce your best creative, strategic and theoretical ideas. The workload replicates the high-pressure environment of senior workplace roles.

Not just more of the same

Postgraduate study is not just ‘more of the same’ undergraduate study. Our experts are there to guide but if you have come from undergraduate study, you will find that postgraduate study demands more in-depth and independent study. It takes you to a new level in knowledge and expertise especially in planning and undertaking research.



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What if your smartphone could recognise that it was you before switching on, and could sense your mood by recognising your facial expressions? What if you… Read more
What if your smartphone could recognise that it was you before switching on, and could sense your mood by recognising your facial expressions? What if you could use a real thumbs-up for 'liking' things on Facebook? How can you play games on an Xbox using only your body gestures? How can you equip cars with in-vehicle technology that could automatically read road signs? 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.

Modules Can Include:
Advanced Transform Methods
Machine Learning
Introduction to Computer Vision
Computer Graphics
Artificial Intelligence
Techniques for Computer Vision
High Performance Computing
C++ for Image Processing
Project

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