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Computer Science×

University College London, Full Time MSc Degrees in Computer Science

We have 23 University College London, Full Time MSc Degrees in Computer Science

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The Computer Science MSc provides a balance between computer science theory and practical software engineering skills, including teamwork for industrial or research clients. Read more

The Computer Science MSc provides a balance between computer science theory and practical software engineering skills, including teamwork for industrial or research clients. Graduates find employment in the IT industry, or complement their first degree subject with computer science knowledge, leading to interdisciplinary industrial positions and PhD research.

About this degree

You will learn fundamental aspects of how computers work by taking modules in computer architecture, operating systems, compilers, data structures and algorithms. You will also gain practical knowledge in areas such as human-computer interaction, App design, databases and software engineering. You will develop programming skills in modern languages, such as object-oriented Java for Android development. 

Team working, project planning and communication skills are developed by working in small groups developing software for real industrial and research clients. Optional modules allow specialisation in subjects such as functional programming, computer music, entrepreneurship and artificial intelligence.

Students undertake modules to the value of 180 credits.

The programme consists of five core modules (75 credits), three optional modules (45 credits) and a research project (60 credits).

Core modules

  • Algorithmics (15 credits)
  • Architecture and Hardware (15 credits)
  • Design (15 credits)
  • Programming (15 credits)
  • Systems Infrastructure (15 credits)

Optional modules

Students must choose a minimum of 15 and a maximum of 45 credits from Group One options. For the remaining credits, students can choose up to 30 credits from Group Two options and up to 15 credits from Electives.

Group One Options (15 to 45 credits)

  • Database Systems (15 credits)
  • Entrepreneurship: Theory and Practice (15 credits)
  • Functional Programming (15 credits)
  • Interaction Design (15 credits)
  • Software Engineering (15 credits)

Group Two Options (up to 30 credits)

  • Affective Interaction (15 credits)
  • Artificial Intelligence and Neural Computing (15 credits)
  • Project Management (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 students undertake an independent computer-based science project which culminates in a dissertation in the form of a project report.

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 Science MSc

Careers

Graduates from UCL are keenly sought after by the world's leading organisations, and many progress in their careers to secure senior and influential positions. UCL Computer Science graduates are particularly valued as a result of the department's strong international reputation, strong links with industry, and ideal location close to the City of London. Our graduates secure careers in a wide variety of organisations; for example with global IT consultancies, as IT analysts with City banks, or as IT specialists within manufacturing industries.

Recent career destinations for this degree

  • Analyst and IT Consultant, KPMG
  • Associate Quantity Developer, Moody's
  • Clinical Systems Manager, Whittington Hospital (NHS)
  • Cyber Security Analyst / Developer, BAE Systems
  • PhD in System Engineering, City University of Hong Kong

Employability

This degree opens up many different career paths. Recent graduates have been employed by some of the world's leading IT companies such as Accenture, Barclays Capital and Credit Suisse. The entrepreneurial spirit is ignited in other students and they may either start their own companies or join dynamic start-ups. Other graduates have gone on to PhD study to conduct cutting-edge research in areas that interest them.

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.

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 Master's programmes have some of the highest employment rates and starting salaries, with graduates entering a wide variety of industries from entertainment to finance.

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.

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.



Read less
This exciting and challenging programme studies how data can be utilised to solve major business and societal challenges. The programme provides students with the knowledge, technical ability and skills for leadership roles in the fields of business analytics and data science. Read more

This exciting and challenging programme studies how data can be utilised to solve major business and societal challenges. The programme provides students with the knowledge, technical ability and skills for leadership roles in the fields of business analytics and data science.

About this degree

The programme is designed to give students multidisciplinary skills in computing (i.e. programming, big data), analytics (i.e. data mining, machine learning, computational statistics, complexity), and business analysis. Emphasis will be on business problem framing, leveraging data as a strategic asset, and communicating complex analytical results to stakeholders.

Students undertake modules to the value of 180 credits.

The programme consists of three core modules (45 credits), four or five optional modules (60 to 75 credits), up to one elective module (15 credits) and a dissertation (60 credits).

Core modules

  • Business Strategy and Analytics (15 credits)
  • Data Analytics (15 credits)
  • Programming for Business Analytics (15 credits)

Optional modules

Students must choose a minimum of 60 and a maxuimum of 75 credits from Optional modules. A maximum of 15 credits may be taken from Electives.

  • Consulting Psychology (15 credits)
  • Consumer Behaviour (15 credits)
  • Data Science for Spatial Systems (15 credits)
  • Decision and Risk (15 credits)
  • Decision and Risk Analysis (15 credits)
  • Group Mini Project: Digital Visualisation (30 credits)
  • Introduction to Machine Learning (15 credits)
  • Mastering Entrepreneurship (15 credits)
  • Statistical Design of Investigations (15 credits)
  • Statistical Models and Data Analysis (15 credits)
  • Talent Management (15 credits)
  • Urban Simulation (15 credits)
  • Web Economics (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

During the summer students will undertake a work placement with a UCL industrial partner. The research and data analysis conducted during this placement will form the basis of a 10,000-word dissertation.

Teaching and learning

The programme is delivered through a combination of lectures by world-class academics and industry leaders, seminars, workshops, tutorials and project work. The programme comprises two terms of taught material, followed by examinations and then a project. Assessment is through unseen written examinations, coursework and the dissertation. 

Further details are available on UCL Computer Science website.

Further information on modules and degree structure is available on the department website: Business Analytics (with specialisation in Computer Science) MSc

Careers

Graduates of UCL Computer Science are particularly valued due to the department's international status and strong reputation for leading research. Recent graduate destinations include such companies as: IBM, Samsung, Microsoft, Price Waterhouse Coopers, Citibank.

Employability

This programme is designed to satisfy the need, both nationally and internationally, for exceptional data scientists and analysts. Graduates will be highly employable in global companies and high-growth businesses, finance and banking organisations, major retail and service companies, and consulting firms. They will be equipped to influence strategy and decision-making, and be able to drive business performance by transforming data into a powerful and predictive strategic asset. We expect our graduates to progress to leading and influential positions in industry.

Why study this degree at UCL?

UCL Computer Science is a global leader in research in experimental computer science. The department scored highest among UK universities for the quality of research in Computer Science and Informatics in the Research Excellence Framework (REF2014), with 96% regarded as 'world-leading' or 'internationally excellent'.

The department consists of a team of world-class academics specialising in big data, computational statistics, machine learning and complexity.

The programme aims to create the next generation of outstanding academics and industry pioneers, who will use data analysis to deliver real social and business impact.

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.



Read less
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.

Read less
Our Software Systems Engineering (SSE) MSc provides an ideal foundation for PhD study. The UCL SSE group is regularly ranked in the top three groups in the world (Microsoft Academic Search), you will be taught by those who are setting the international agenda, and our research has been repeatedly rated as world-class. Read more

Our Software Systems Engineering (SSE) MSc provides an ideal foundation for PhD study. The UCL SSE group is regularly ranked in the top three groups in the world (Microsoft Academic Search), you will be taught by those who are setting the international agenda, and our research has been repeatedly rated as world-class. Fully-funded PhD scholarships are available for high-performing students.

About this degree

Students are trained in the principles and techniques of engineering large, complex software systems and gain the opportunity to apply these techniques in a realistic group project setting. The programme analyses current practice in software systems engineering, looking at the most significant trends, problems and results in complex software systems.

Students undertake modules to the value of 180 credits.

The programme consists of five core modules (75 credits), and either a group project (60 credits) or three research modules (90 credits) including a project. Students will be able to select between one and three modules (15 to 45 credits) from electives.

Core modules

  • Professional Practice (15 credits)
  • Requirements Engineering and Software Architecture (15 credits)
  • Software Abstractions and Systems Integration (15 credits)
  • Tools and Environments (15 credits)
  • Validation and Verification (15 credits)

Optional modules

Students must take either the Group Project in Software Systems Engineering (60 credits) with 45 credits from electives, or Research Methods, Project and Seminar in Software Engineering (90 credits) with 15 credits from electives.

  • Research Methods in Software Engineering (15 credits)
  • Research Project in Software Engineering (60 credits)
  • Research Seminar in Software Engineering (15 credits)
  • OR
  • Group Project in Software Systems Engineering (60 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 UCL Computer Science webpage.

Dissertation/report

Most students participate in a group industrial project, generally in close collaboration with one of our industrial partners. 

Other students undertake either an individual or small-group research project, under the supervision of academics in UCL's Software Systems Engineering group.

Teaching and learning

The programme is delivered through a combination of lectures, written and laboratory exercises, and group project supervision. Student performance is assessed through written exercises with modelling notations, laboratory exercises with tools and environments, unseen examination papers, and a significant, comprehensive group project.

Further information on modules and degree structure is available on the department website: Software Systems Engineering MSc

Funding

The department typically does not hire postgraduate students on research or teaching assistantships because the students need to work full-time on their studies for the programme.

Four MSc Scholarships, worth £4000 each, are made available by UCL Computer Science to UK/EU offer holders with a record of excellent academic achievement. The closing date is 30 June 2018. For more information, please see the department pages.

For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website.

Careers

This professionally oriented programme provides an ideal foundation for graduates who wish to pursue a career as a software architect or leader of software development organisations. It also provides an excellent introduction for those who want to pursue research in software systems engineering.

Graduates from UCL are keenly sought after by the world's leading organisations, and many progress in their careers to secure senior and influential positions. UCL Computer Science (UCL-CS) graduates are particularly valued as a result of the department's strong international reputation, strong links with industry, and ideal location close to the City of London.

Graduates have found positions at global companies such as Barclays and RBS.

Recent career destinations for this degree

  • Software Developer, BNP Paribas
  • Technology Analyst, Morgan Stanley
  • IT Consultant, OnTrack
  • Software Analyst and Designer, Nok Technology
  • Security Science, UCL

Employability

There is, throughout the world, a strong demand for software engineers with solid foundations covering not only the programming aspects of software development, but also aspects related to requirements engineering, software architectures, system integration, and testing.

Following graduation, our students are generally hired as software engineers or software architects, sometimes by companies they have engaged with in the context of their MSc project.

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.

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 Master's programmes have some of the highest employment rates and starting salaries, with graduates entering a wide variety of industries from entertainment to finance.

We take an experimental approach to our subject and place a high value on our extensive range of industrial collaborations. In the recent past, students have worked on projects and coursework in collaboration with Microsoft, IBM, JP Morgan, Citigroup and BNP Paribas.

Accreditation:

CITPFL - Accredited by BCS. CEng (partial fulfilment) - Accreditation by the BCS.



Read less
The Information Security MSc offers a specialist programme designed to provide a fundamental understanding of information security and to convey practical engineering skills. Read more

The Information Security MSc offers a specialist programme designed to provide a fundamental understanding of information security and to convey practical engineering skills. There are good prospects for highly trained information security professionals and there is a shortage of trained personnel in this area.

About this degree

Students develop an advanced knowledge of information security and an awareness of the context in which information security operates in terms of safety, environmental, social and economic aspects. They gain a wide range of intellectual, practical and transferable skills, enabling them to develop a flexible professional career in IT.

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 Security I (15 credits)
  • Computer Security II (15 credits)
  • Introduction to Cryptography (15 credits)
  • Research in Information Security (15 credits)

Optional modules

Students choose 60 credits from the following:

  • Applied Cryptography (15 credits)
  • Cryptanalysis (15 credits)
  • Cybercrime (15 credits)
  • Distributed Systems and Security (15 credits)
  • Information Security Management (15 credits)
  • Language Based Security (15 credits)
  • Malware (15 credits)
  • People and Security (15 credits)
  • Philosophy, Politics and Economics of Security and Privacy (15 credits)
  • Privacy Enhancing Technologies (15 credits)

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

Dissertation/report

All MSc students undertake an independent research project which culminates in a dissertation (maximum length of 120 pages) and an oral presentation.

Teaching and learning

The programme is delivered through a combination of lectures, seminars, problem classes, tutorials, laboratory classes and projects. Assessment is through written examinations, presentations, vivas, tests, coursework, written reports, formal presentations and the research project.

Further information on modules and degree structure is available on the department website: Information Security MSc

Careers

UCL graduates are keenly sought after by the world's leading organisations. UCL Computer Science graduates are particularly valued as a result of the department's strong international reputation, strong links with industry, and ideal location close to the City of London. Our graduates secure careers in a wide variety of organisations, e.g. with global IT consultancies, as IT analysts with City banks, or as IT specialists within manufacturing industries.

Recent career destinations for this degree

  • Information Security Expert, State Oil Company of Azerbaijan Republic
  • IT Risk and Cyber Security Associate, PwC
  • PhD Research Student in Computer Science, UCL
  • Security Engineer, Morgan Stanley
  • Technical Analyst, The Royal Bank of Scotland (RBS)

Employability

Some of the brightest alumni of the degree go on to careers in academia. The majority of our students take jobs in the software and consultancy industries, usually in a security-related role such as security standards compliance, secure software design or security consultancy. Students have the opportunity to do industrially based projects with companies such as BT and McAfee. The department is recognised as an academic centre of excellence on cyber security and further opportunities to expand both academic and industrial contacts arise through the ACE-CS guest lecture series integrated into the degree.

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.

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).

This MSc is taught by academics who conduct world-leading research, most notably in cryptography and human-centred approaches to security, privacy and trust. Access to industry-led projects and guest lecturers from academia and industry will enhance post-graduation opportunities for careers in security-related research, or employment in cyber security roles. 

UCL's central London location enables students to enjoy the full benefits of life in a vibrant world city with easy access to excellent scientific and cultural centres.

Accreditation

Information Security has been successfully awarded full certification from the National Cyber Security Centre (NCSC). Students who wish to gain the certification with their degree need to choose COMPGA14 Information Security Management as one of their optional modules.

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.



Read less
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.



Read less
This MSc provides an ideal foundation for graduates who wish to pursue a career as software engineers. Read more

This MSc provides an ideal foundation for graduates who wish to pursue a career as software engineers. The programme provides the opportunity to undertake a significant group software engineering project sponsored by a financial services company, allowing students to specialise in software systems engineering from a financial computing perspective.

About this degree

Students gain instruction in all aspects of software engineering needed for the development of large, complex, highly dynamic, distributed software-intensive systems. The programme covers requirements engineering, software design, validation and verification, tools for the development of software intensive systems, and provides instruction in financial information systems.

Students undertake modules to the value of 180 credits.

The programme consists of six core modules (90 credits), one optional module (15 credits), one elective module (15 credits) and a group project (60 credits).

Core modules

  • Financial Institutions and Markets (15 credits)
  • Professional Practice (15 credits)
  • Requirements Engineering and Software Architecture (15 credits)
  • Software Abstractions and Systems Integration (15 credits)
  • Tools and Environments (15 credits)
  • Validation and Verification (15 credits)

Optional modules

Students are required to select 15 credits from the Option group and 15 credits from the Elective group.

Option Group

  • Compliance, Risk and Regulation (15 credits)
  • Financial Market Modelling and Analysis (15 credits)

Elective Group

  • Complex Networks and Web (15 credits)
  • Computer Security I (15 credits)
  • Computer Security II (15 credits)
  • Distributed Systems and Security (15 credits)
  • Introduction to Logic, Semantics and Verification (15 credits)
  • Language Based Security (15 credits)
  • Malware (15 credits)
  • Modal Logic and Transition Systems (15 credits)
  • Multimedia Systems (15 credits)
  • Networked Systems (15 credits)
  • People and Security (15 credits)
  • Verification and Mechanised Proofs (15 credits)

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

Dissertation/report

All students participate in a group project, encompassing the full software development lifecycle and applying techniques learned, such as the technical skills of analysis, design and implementation.

Teaching and learning

The programme is delivered through a combination of lectures, written and laboratory exercises, and project work. Student performance is assessed through written exercises with modelling notations, laboratory exercises with tools and environments, unseen examination papers, and a significant, comprehensive group project.

Further information on modules and degree structure is available on the department website: Financial Systems Engineering MSc

Careers

This professionally oriented programme provides an ideal foundation for graduates who wish to pursue a career as a software architect or leader of software development organisations. It also provides an excellent introduction for those who want to pursue research in software systems engineering.

Graduates from UCL are keenly sought by the world's leading organisations, and many progress in their careers to secure senior and influential positions. UCL Computer Science graduates are particularly valued as a result of the department's international reputation, strong links with industry, and ideal location close to the City of London.

Graduates have found positions at global companies such as RBS and UBS.

Employability

There is, throughout the world, a strong demand for software engineers with solid foundations covering not only the programming aspects of software development, but also aspects related to requirements engineering, software architectures, system integration, and testing. Many surveys rank software engineering positions as among the best jobs in the world.

Following graduation, our students are generally hired as software engineers or software architects by large financial institutions, sometimes by institutions they have engaged with in the context of their MSc project.

Why study this degree at UCL?

UCL Computer Science is recognised as a world leader in teaching and research.

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 Master's programmes have some of the highest employment rates and starting salaries, with graduates entering a wide variety of industries from entertainment to finance.

We take an experimental approach to our subject and place a high value on our extensive range of industrial collaborations. In the recent past, students have worked on projects and coursework in collaboration with Microsoft, IBM, and financial institutions such as JP Morgan, Citigroup and BNP Paribas.

Accreditation

IET - Partial CEng (Further Learning). CITPFL - Accredited by BCS. CEng (partial fulfilment) - Accreditation by the BCS.

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.



Read less
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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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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Students develop an advanced knowledge of computational methods in finance, which is a prerequisite for a successful career in the financial industry within 'quant' teams. Read more

Students develop an advanced knowledge of computational methods in finance, which is a prerequisite for a successful career in the financial industry within 'quant' teams. 'Quants' (development analysts) design and implement complex models and are sought after by banks, fund managers, insurance companies, hedge funds, and financial software and data providers.

About this degree

This degree comprises advanced modules on quantitative and modelling skills, which are essential for 'quant' roles in trading research, regulation and risk. This applied MSc programme is distinctive in that it provides a solid mathematical and statistical foundation together with an education in advanced-level programming.

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 dissertation (60 credits).

Core modules

  • Financial Data and Statistics (15 credits)
  • Financial Market Modelling and Analysis (15 credits)
  • Market Risk Measures and Portfolio Theory (15 credits)
  • Numerical Analysis for Finance (15 credits)

Optional modules

Students select 60 credits from optional modules.

  • Algorithmics (15 credits)
  • Applied Computational Finance (15 credits)
  • Database Systems (15 credits)
  • Financial Engineering (15 credits)
  • Financial Institutions and Markets (15 credits)
  • Machine Learning with Applications in Finance (15 credits)
  • Market Microstructure (15 credits)
  • Networks and Systemic Risk (15 credits)
  • Operational Risk Measurement for Financial Institutions (15 credits)
  • Software Engineering (15 credits)
  • Stochastic Processes for Finance (15 credits)

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

With permission, a student may substitute up to two optional modules with electives. 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 about 10,000 words or 50 pages. Usually this will be undertaken during a summer placement in an industry environment arranged by the department.

Teaching and learning

The programme is delivered through a combination of lectures, tutorials, seminars, and project work. It comprises two terms of teaching, followed by examinations and a dissertation. Assessment is through coursework, unseen examinations and a dissertation.

Further information on modules and degree structure is available on the department website: Computational Finance MSc

Careers

This is a relatively new programme and therefore no specific information on graduate destinations is currently available. UCL Computer Science graduates typically find work in financial institutions such as Credit Suisse, JP Morgan, Morgan Stanley, and Deutsche Bank as financial analyst application developers, quant developers, and business managers. The University of Cambridge and UCL are among top further study destinations.

Employability

Our graduates are particularly valued as a result of the department's international reputation, strong links with industry, and ideal location close to the City of London. Graduates are especially sought after by leading finance companies and organisations.

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 hosts the Doctoral Training Centre in Financial Computing and Analytics, which is the only one of its kind in the UK.

UCL's central London location ideally places it close to one of the world's most important financial centres, with which UCL pioneers industrial/academic engagements. Students on the Computational Finance MSc will benefit from teaching input from City of London practitioners.

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 for data-driven problem solving, which is in increasing demand in fields such as marketing, pharmaceutics, finance and management. Read more

Data science brings together computational and statistical skills for data-driven problem solving, which is in increasing demand in fields such as marketing, pharmaceutics, finance and management. This MSc will equip students with the analytical tools to design sophisticated technical solutions using modern computational methods and with an emphasis on rigorous statistical thinking.

About this degree

The programme combines training in core statistical and machine learning methodology, beginning at an introductory level, with a range of optional modules covering more specialised knowledge in statistical computing and modelling. Students choosing the statistics specialisation will take one compulsory module and up to two additional modules in computer science, with the remaining modules (including the research project) taken mainly from within UCL Statistical Science.

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 dissertation/report (60 credits).

Core modules

  • Introduction to Statistical Data Science
  • Introduction to Machine Learning
  • Statistical Design of Investigations
  • Statistical Computing

Optional modules

At least two from a choice of Statistical Science modules including:

  • Applied Bayesian Methods
  • Decision & Risk
  • Factorial Experimentation
  • Forecasting
  • Quantitative Modelling of Operational Risk and Insurance Analytics
  • Selected Topics in Statistics
  • Stochastic Methods in Finance I
  • Stochastic Methods in Finance II
  • Stochastic Systems

Up to two from a choice of Computer Science modules including:

  • Affective Computing and Human-Robot Interaction
  • Graphical Models
  • Statistical Natural Language Processing
  • Information Retrieval & Data Mining

Dissertation/report

All students undertake an independent research project, culminating in a dissertation usually of 10,000-12,000 words. Workshops running during the teaching terms provide preparation for this project and cover the communication of statistics.

Teaching and learning

The programme is delivered through a combination of lectures, tutorials and classes, some of which are dedicated to practical work. Assessment is through written examination and coursework. The research project is assessed through the dissertation and a 15-minute presentation.

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

Careers

Graduates from UCL Statistical Science typically enter professional employment across a broad range of industry sectors or pursue further academic study. 

The Data Science MSc is a new programme with the first cohort of students due to graduate in 2017. Recent career destinations for graduates of the related Statistics MSc include:

  • Management Associate, HSBC
  • Statistical Analyst, Nielsen
  • PhD in Statistics, UCL
  • Mortgage Specialist, Citibank
  • Research Assistant Statistician, Cambridge Institute of Public Health

Employability

Data science professionals are likely to be increasingly sought after as the integration of statistical and computational analytical tools becomes essential in all kinds of organisations and enterprises. A thorough understanding of the fundamentals is to be expected from the best practitioners. For instance, in applications in marketing, the healthcare industry and banking, computational skills should should be accompanied by statistical expertise at graduate level. Data scientists need a broad background knowledge so that they will be able to adapt to rapidly evolving challenges. Recent graduates from the related Statistics MSc have been offered positions as research analysts or consultants, and job opportunities in these areas are increasing.

Why study this degree at UCL?

UCL Statistical Science has a broad range of research interests, but has particular strengths in the area of computational statistics and in the interface between statistics and computer science.

UCL's Centre for Computational Statistics and Machine Learning, in which many members of the department are active, has a programme of seminars, masterclasses and other events. UCL's Centre for Data Science and Big Data Institute are newer developments, again with strong involvement of the department, where emphasis is on research into big data problems.

UCL is one of the founding members of the Alan Turing Institute, and both UCL Statistical Science and UCL Computer Science will be playing major roles in this exciting new development which will make London a major focus for big data 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: Statistical Science

82% 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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Robotics and autonomous systems (RAS) are set to shape innovation in the 21st century, underpinning research in a wide range of challenging areas. Read more

Robotics and autonomous systems (RAS) are set to shape innovation in the 21st century, underpinning research in a wide range of challenging areas: the ageing population, efficient health care, safer transport, and secure energy. The UCL edge in scientific excellence, industrial collaboration and cross-sector activities make it ideally placed to drive IT robotics and automation education in the UK.

About this degree

The programme provides an overview of robotic and computational tools for robotics and autonomous systems as well as their main computational components: kinetic chains, sensing and awareness, control systems, mapping and navigation. Optional modules in machine learning, human-machine interfaces and computer vision help students grasp fields related to robotics more closely, while the project thesis allows students to focus on a specific research topic in depth.

Students undertake modules to the value of 180 credits.

The programme consists of four core modules (60 credits), two to four optional modules (30 to 60 credits), up to two elective modules (30 credits), and a dissertation/report (60 credits).

Core modules

  • Robotic Control Theory and Systems (15 credits)
  • Robotic Sensing, Manipulation and Interaction (15 credits)
  • Robotic Systems Engineering (15 credits)
  • Robotic Vision and Navigation (15 credits)

Optional modules

Students will need to choose a minimum of 30 and a maximum of 60 credits from the optional modules.

  • Acquisition and Processing of 3D Geometry (15 credits)
  • Artificial Intelligence and Neural Computing (15 credits)
  • Image Processing (15 credits)
  • Inverse Problems in Imaging (15 credits)
  • Mathematical Methods, Algorithmics and Implementations (15 credits)
  • Numerical Optimisation (15 credits)
  • Research Methods and Reading (15 credits)
  • Terrestrial Data Acquisition (15 credits)

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

Students can also choose up to two elective MSc modules from across UCL Computer Science, UCL Mechanical Engineering and UCL Bartlett School of Architecture.

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 12,000 words.

Teaching and learning

Teaching is delivered by lectures, tutorials, practical sessions, projects and seminars. Assessment is through examination, individual and group projects and presentations, and design exercises.

Further information on modules and degree structure is available on the department website: Robotics and Computation MSc

Funding

Four MSc Scholarships, worth £4000 each, are made available by the Department of Computer Science to UK/EU offer holders with a record of excellent academic achievement. The closing date will be in June 2018. For more information, please see the department pages.

For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website.

Careers

Robotics is a growing field encompassing many technologies with applications across different industrial sectors, and spanning manufacturing, security, mining, design, transport, exploration and healthcare. Graduates from our MSc programme will have diverse job opportunities in the international marketplace with their knowledge of robotics and the underpinning computational and analytical fundamentals that are highly valued in the established and emerging economies. Students will also be well placed to undertake PhD studies in robotics and computational research specific to robotics but translational across different analytical disciplines or applied fields that will be influenced by new robotic technologies and capabilities.

Employability

This programme prepares students to enter a robotics-related industry or any other occupation requiring engineering or analytical skills. Graduates with skills to develop new robotics solutions and solve computational challenges in automation are likely to be in demand globally.

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).

With the external project involvement anticipated, students on this programme will have the opportunity to interact and collaborate with key companies in the industry - Airbus, Shadow Hand, OC Robotics and Intuitive Surgical - and work on real-world problems through industry-supported projects.

Recent investment across UCL in the Faculty of Engineering and The Bartlett Faculty of the Built Environment has created the infrastructure for an exciting robotics programme, which will be interdisciplinary and unique within the UK and Europe.



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The MSc covers web-related technologies and big data analytics. It is intended for students with a general science and engineering background and is an ideal preparation for roles with some of the best internet-related industries and areas requiring big data analytical skills. Read more

The MSc covers web-related technologies and big data analytics. It is intended for students with a general science and engineering background and is an ideal preparation for roles with some of the best internet-related industries and areas requiring big data analytical skills.

About this degree

Students will gain a detailed knowledge and understanding of web-related technologies and big data analytics, ranging from information search and retrieval, natural language processing, data mining and knowledge acquisition, large-scale distributed data analytics and cloud computing to e-commerce and their business economic models and the latest concepts of social networks.

MSc students undertake modules to the value of 180 credits.

The programme consists of three core modules (45 credits), five optional modules (75 credits), and the research dissertation (60 credits).

Core modules

  • Complex Networks and Web (15 credits)
  • Information Retrieval and Data Mining (15 credits)
  • Web Economics (15 credits)

Optional modules

Students must choose a minimum of 45 and a maximum of 75 credits of optional modules. Up to two electives (30 credits) may also be chosen instead of two of the optional modules.

  • Affective Computing and Human-Robot Interaction (15 credits)
  • Applied Machine Learning (15 credits)
  • Birkbeck College: Cloud Computing (15 credits)
  • Computer Graphics (15 credits)
  • Entrepreneurship: Theory and Practice (15 credits)
  • Graphical Models (15 credits)
  • Interaction Design (15 credits)
  • Machine Vision (15 credits)
  • Probabilistic and Unsupervised Learning (15 credits)
  • Statistical Natural Language Processing (15 credits)
  • Supervised Learning (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 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 help from demonstrators. Student performance is assessed by unseen written examinations, coursework and the dissertation.

Careers

Graduates from UCL are keenly sought after by the world's leading organisations, and many progress in their careers to secure senior and influential positions. Graduates of our Web Science and Big Data Analytics programmes are expected to develop careers in scientific research, the internet-based industry and other professional areas that require big data analytics skills.

Recent career destinations for this degree

  • CEO (Chief Executive Officer), Hoxton Analytics
  • Software Engineer, China Mobile
  • Computer Science Lecturer, Singapore Polytechnic
  • Software Developer, Barclays
  • Software Engineer, UCL

Employability

The MSc has a unique industry connection as almost all our lecturers have industry experience. Through long-term collaborations with big players in the field such as Google, Microsoft and BT, research is driven by the fundamental technical challenges faced by the industry. Throughout the degree, our students have the chance to interact with our industry collaborators and previous students, and have placement opportunities to address the specific technical problems faced by the industry.

Why study this degree at UCL?

UCL Computer Science is recognised as a world leader in teaching and research.

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 Master's programmes have some of the highest employment rates and starting salaries, with graduates entering a wide variety of industries, from entertainment to finance.

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.



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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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This MSc forms the second year of the dual Master's degree of the European Institute of Innovation and Technology (EIT). The programme offers an advanced ICT engineering education together with a business minor focused on innovation and entrepreneurship. Read more
This MSc forms the second year of the dual Master's degree of the European Institute of Innovation and Technology (EIT). The programme offers an advanced ICT engineering education together with a business minor focused on innovation and entrepreneurship. Students will spend their first year at one of the EIT's partner universities in Europe, and can elect to spend their second year at UCL.

See the website http://www.ucl.ac.uk/prospective-students/graduate/taught/degrees/ict-innovation-msc

Key Information

- Application dates
All applicants:
Open: 5 October 2015
Close: 15 February 2016
Fees note: UK/EU full-time fee available on request from the department

English Language Requirements

If your education has not been conducted in the English language, you will be expected to demonstrate evidence of an adequate level of English proficiency.
The English language level for this programme is: Good
Further information can be found on http://www.ucl.ac.uk/prospective-students/graduate/life/international/english-requirements .

International students

Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from http://www.ucl.ac.uk/prospective-students/international .

Degree Information

The thematic core foundations together with modules on Innovation and Entrepreneurship will be taught during the first year. In the second year at UCL, the programme focuses on the two thesis projects and on specialised taught modules. Students at UCL will choose either Human Computer Interaction and Design (HCID) or Digital Media Technology (DMT) as their major specialisation.

This two year dual masters degree has an overall credit value of 120 ECTS.

Students take modules to the value of 60 ECTS (150 Credits) in their second year at UCL, consisting of four taught modules (60 credits), a minor thesis (15 credits) and a master's thesis (75 credits).

- Core Modules
Technical Major: Human-Computer Interaction and Design:
Ergonomics for Design
Affective Interaction
Minor Thesis on Innovation and Entrepreneurship
Master's Thesis

Technical Major: Digital Media Technology:
Virtual Environments
Advanced Modelling, Rendering and Animation
Computational Photography and Capture
Minor Thesis on Innovation and Entrepreneurship
Master's Thesis

- Options
Technical Major: Human-Computer Interaction and Design:
Affective Computing and Human-robot Interaction
Societechnical Systems: IT and the Future of Work
Interfaces and Interactivity
Qualitative Research Methods
Virtual Environments

Technical Major: Digital Media Technology:
Machine Vision
Geometry of Images
Image Processing
Computational Modelling for Biomedical Imaging
Acquisition and Processing of 3D Geometry
Multimedia Systems
Network and Application Programming
Interaction Design
Professional Practice

- Dissertation/report
All MSc students undertake a minor thesis and a master's thesis, in collaboration with an external partner. For the master's thesis, students will spend at least two months in the external partner's environment.

Teaching and Learning

The programme is delivered through a combination of lectures, discussions, practical sessions, case studies, problem-based learning and project work. Assessment is through coursework assignments, unseen examinations and the two thesis projects.

Further information on modules and degree structure available on the department web site ICT Innovation MSc http://www.cs.ucl.ac.uk/admissions/msc_ict_innovation/

Funding

Scholarships relevant to this department are displayed (where available) below. For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website http://www.ucl.ac.uk/prospective-students/scholarships .

- Brown Family Bursary - NOW CLOSED FOR 2015/16 ENTRY
Value: £15,000 (1 year)
Eligibility: UK students
Criteria: Based on both academic merit and financial need

- Computer Science Excellence Scholarships
Value: £4,000 (1)
Eligibility: UK, EU students
Criteria:

More scholarships are listed on the Scholarships and Funding website http://www.ucl.ac.uk/prospective-students/scholarships

Careers

Graduates of this programme will have the key skills in innovation and entrepreneurship necessary for the international market, together with a solid foundation in the technical topics that drive the modern technological economy.

Why study this degree at UCL?

The EIT Digital Master School is a European initiative designed to turn Europe into a global leader in ICT innovation, fostering a partnership between leading companies, research centres and technical universities in Europe.

The school offers two-year programmes where you can choose two universities in two different European institutes to build a curriculum of your choice based on your skills and interest. We offer double degrees, which combine technical competence with a set of skills in innovation and entrepreneurship. While you get an excellent theoretical education, you also get the opportunity to work with leading European research institutes and leading business partners.

Student / staff ratios › 200 staff including 120 postdocs › 650 taught students › 175 research students

Application and next steps

- Applications
Students are advised to apply as early as possible due to competition for places. Those applying for scholarship funding (particularly overseas applicants) should take note of application deadlines.

- Who can apply?
This programme is suitable for students with a relevant degree who wish to develop key skills in innovation and entrepreneurship together with an advanced education in ICT engineering, for a future career or further study in this field.

The admission procedure for the EIT ICT Labs Master's programme is organised centrally from Sweden by the KTH Admissions Office. Please read the admission requirements and application instructions before sending your documents. For further details of how to apply please visit http://www.eitictlabs.masterschool.eu/programme/application-admission/application-instructions.
Please note that applications after the deadline may be accepted. Late applications will be processed subject to time, availability and resources.

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