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Masters Degrees (Machine)

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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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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.



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.



Read less
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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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
Machine learning has already revolutionised the user experience of millions of web users the world over, and yet the discipline is still comparatively young. Read more

Machine learning has already revolutionised the user experience of millions of web users the world over, and yet the discipline is still comparatively young. In time, this form of artificial intelligence will have an even more profound impact on the way we use software and interact with computer technology. Study Machine Learning at Royal Holloway, University of London and you’ll equip yourself with a set of crucial skills to assist in the development of the next generation of search and analysis technologies.

You’ll study in one of the UK’s leading research departments, and contribute to our renowned research culture with your own Independent Project. You’ll benefit from cutting-edge research-led teaching, with the department’s research strengths including Algorithms and Applications, Machine Learning, Bioinformatics and others.

Royal Holloway’s location close to the M4 corridor – otherwise known as ‘England’s Silicon Valley’ – gives you the chance to benefit from networking and placement opportunities with some of the country’s top technology organisations. This flexible programme is also available with a year in industry option, helping you to gain invaluable skills and experience to take into your future career.

You’ll graduate with a highly desirable Masters qualification in a rapidly expanding sector with excellent graduate employability prospects. The skills and knowledge you’ll develop will be in high demand by employers including Google, Facebook, Microsoft and Yahoo, and you'll be well prepared to pursue a rewarding career.

  • Study in a department renowned for research excellence, ranked 11th in the UK for the quality of its research publications (Research Excellence Framework 2014).
  • Benefit from strong industry ties, with close proximity to ‘England’s Silicon Valley’.
  • Graduate with a Masters degree offering excellent graduate employability prospects.
  • Tailor your learning with a wide range of engaging optional modules.
  • Choose from a one-year programme structure or an optional year in industry.

Course structure

Core modules

  • Data Analysis
  • Computation with Data
  • Programming for Data Analysis
  • Machine Learning
  • On-line Machine Learning
  • Inference
  • Applied Probability
  • Individual Project

Optional modules

In addition to these mandatory course units there are a number of optional course units available during your degree studies. The following is a selection of optional course units that are likely to be available. Please note that although the College will keep changes to a minimum, new units may be offered or existing units may be withdrawn, for example, in response to a change in staff. Applicants will be informed if any significant changes need to be made.

  • Database Systems
  • Large-Scale Data Storage and Processing
  • Methods of Computational Finance
  • Software Verification
  • Advanced Data Communications
  • Fundamentals of Digital Sound and Music
  • Intelligent Agents and Multi-Agent Systems
  • Semantic Web
  • Internet and Web Technologies
  • Large-Scale Data Storage and Processing
  • Service-Oriented Computing, Technology and Management
  • Business Intelligence
  • Business Intelligence Systems, Infrastructures and Technologies
  • Computational Optimisation
  • Methods of Bioinformatics
  • Visualisation and Exploratory Analysis
  • Financial Econometrics
  • Investment and Portfolio Management
  • Fixed Income Securities and Derivatives
  • Microeconometrics
  • Decision Theory and Behaviour
  • Security Technologies
  • Introduction to Cryptography and Security Mechanisms
  • Network Security
  • Computer Security (Operating Systems)
  • Security Management
  • Smart Cards, RFIDs and Embedded Systems Security. 
  • Digital Forensics
  • Security Testing - Theory and Practice
  • Software Security
  • Database Security
  • Cyber Security

Teaching & assessment

Assessment is carried out by a variety of methods including coursework and a dissertation. The placement is assessed as part of your degree.

Your future career

A Masters in Machine Learning at Royal Holloway, University of London offers students excellent graduate employability prospects. You’ll develop practical skills in Machine Learning Techniques, making you an attractive candidate to employers. You’ll also be well-placed to pursue PhD study, should you choose to progress your studies further.

Our recent alumni have gone on to enjoy rewarding careers in a variety of computer science-related roles, including network systems design and engineering, web development and production. Our proximity to the M4 corridor technology hub – dubbed ‘England’s Silicon Valley’ – gives students the chance to enjoy excellent networking and placement opportunities with some of the country’s top technology organisations. 

  • 90% of Royal Holloway graduates in work or further education within six months of graduating.
  • Strong industry ties help to provide placement and networking opportunities with some of the country’s leading institutions.
  • On-site College Careers Service provides help and support for students. 


Read less
Machine Learning has already revolutionised the user experience of millions of web users the world over, and yet the discipline is still comparatively young. Read more

Machine Learning has already revolutionised the user experience of millions of web users the world over, and yet the discipline is still comparatively young. In time, this form of artificial intelligence will have an even more profound impact on the way we use software and interact with computer technology. Study Machine Learning with a Year in Industry at Royal Holloway, University of London and you’ll equip yourself with a set of crucial skills to assist in the development of the next generation of search and analysis technologies. 

You’ll study in one of the UK’s leading research departments, and contribute to our renowned research culture with your own Independent Project. You’ll benefit from cutting-edge research-led teaching, with the department’s research strengths including Algorithms and Applications, Machine Learning, Bioinformatics and others. 

By electing to spend a year in business you will also be able to integrate theory and practice and gain real business experience. In the past, our students have secured placements in blue-chip companies such as Centrica, Data Reply, Disney, IMS Health, Rolls Royce, Shell, Sociéte Générale, VMWare and UBS, among others.

Royal Holloway’s location close to the M4 corridor – otherwise known as ‘England’s Silicon Valley’ – gives you the chance to benefit from networking and placement opportunities with some of the country’s top technology organisations. This flexible programme includes a rewarding year in industry, helping you to gain invaluable skills and experience to take into your future career.

You’ll graduate with a highly desirable Masters qualification in a rapidly expanding sector with excellent graduate employability prospects. The skills and knowledge you’ll develop will be in high demand by employers, and you'll be well prepared to pursue a rewarding career in the field of your choosing.

  • Study in a department renowned for research excellence, ranked 11th in the UK for the quality of its research publications (Research Excellence Framework 2014).
  • Gain invaluable skills and experience with a year in industry at one of the country's leading tech organisations.
  • Benefit from strong industry ties, with close proximity to ‘England’s Silicon Valley’.
  • Graduate with a Masters degree offering excellent graduate employability prospects.
  • Tailor your learning with a wide range of engaging optional modules.

Course structure

Core modules

Year 1

  • Data Analysis
  • Computation with Data
  • Programming for Data Analysis
  • Machine Learning
  • On-line Machine Learning
  • Inference
  • Applied Probability

Year 2

You will spend this year on a work placement. You will be supported by the Department of Computer Science and the Royal Holloway Careers and Employability Service to find a suitable placement. This year forms an integral part of the degree programme and you will be asked to complete assessed work. The mark for this work will count towards your final degree classification.

  • Individual Project

Optional modules

In addition to these mandatory course units there are a number of optional course units available during your degree studies. The following is a selection of optional course units that are likely to be available. Please note that although the College will keep changes to a minimum, new units may be offered or existing units may be withdrawn, for example, in response to a change in staff. Applicants will be informed if any significant changes need to be made.

  • Database Systems
  • Large-Scale Data Storage and Processing
  • Methods of Computational Finance
  • Software Verification
  • Advanced Data Communications
  • Fundamentals of Digital Sound and Music
  • Semantic Web
  • Internet and Web Technologies
  • Large-Scale Data Storage and Processing
  • Service-Oriented Computing, Technology and Management
  • Business Intelligence
  • Business Intelligence Systems, Infrastructures and Technologies
  • Computational Optimisation
  • Methods of Bioinformatics
  • Visualisation and Exploratory Analysis
  • Financial Econometrics
  • Investment and Portfolio Management
  • Fixed Income Securities and Derivatives
  • Microeconometrics
  • Decision Theory and Behaviour
  • Security Technologies
  • Introduction to Cryptography and Security Mechanisms
  • Network Security
  • Computer Security (Operating Systems)
  • Security Management
  • Smart Cards, RFIDs and Embedded Systems Security
  • Digital Forensics
  • Security Testing - Theory and Practice
  • Software Security
  • Database Security
  • Cyber Security

Teaching & assessment

Assessment is carried out by a variety of methods including coursework and a dissertation. The placement is assessed as part of your degree.

Your future career

A Masters in Machine Learning with a Year in Industry at Royal Holloway, University of London offers students excellent graduate employability prospects. You’ll develop practical skills in machine Learning Techniques, making you an attractive candidate to employers and gain invaluable skills, experience and connections during your year in industry. You’ll also be well-placed to pursue PhD study, should you choose to progress your studies further.

Our recent alumni have gone on to enjoy rewarding careers in a variety of computer science-related roles, including network systems design and engineering, web development and production. Our proximity to the M4 corridor technology hub – dubbed ‘England’s Silicon Valley’ – gives students the chance to enjoy excellent networking and placement opportunities with some of the country’s top technology organisations. 

  • 90% of Royal Holloway graduates in work or further education within six months of graduating.
  • Strong industry ties help to provide placement and networking opportunities with some of the country’s leading institutions.
  • On-site College Careers Service provides help and support for students. 


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This is a twelve-month MPhil programme, taught from within our Information Engineering Division, with a unique, joint emphasis on both machine learning and on speech and language technology. Read more
This is a twelve-month MPhil programme, taught from within our Information Engineering Division, with a unique, joint emphasis on both machine learning and on speech and language technology. The course aims: to teach the state of the art in machine learning, speech and language processing; to give students the skills and expertise necessary to take leading roles in industry; to equip students with the research skills necessary for doctoral study.

See the website http://www.graduate.study.cam.ac.uk/courses/directory/egegmpmsl

Course detail

By the end of the programme, students will have acquired:

- a knowledge of the fundamental techniques in machine learning and how to apply these techniques to a range of practical problems;
- a deep understanding of the fundamental problems in speech and language processing and the technologies that form the current state-of-the art;
- a comprehensive understanding of techniques, and a thorough knowledge of the literature, applicable to the area of their chosen research topic;
- presentation skills through presenting their research in progress;
- the methodological and other technical skills necessary for research in their chosen area;
- the ability to critically assess the technical literature in machine learning and speech and language processing and related topics;
- directly marketable skills in computing, speech and language processing, machine learning, and the data sciences;
- collaborative skills through working with other students on the practical exercises and with PhD students and Research Assistants while carrying out their research project;
- experience in large-scale computing for machine learning and speech and language processing;
- an understanding of how to define and conduct a research project.

Format

Students will spend the Michaelmas and Lent terms undertaking taught course modules. There will be an equivalent of ten 'full' core modules (ie, equivalent to a 16 lecture course), in addition to an elective option which can be chosen from a broad variety of modules. From mid-Lent term through the end of the course, students will conduct a substantial research project leading to a dissertation.

The taught modules will be in a range of styles: traditional lecture courses, lecture courses with associated practical classes, reading clubs, and seminar style modules. The course will emphasize coursework in several of the taught modules. Software projects aimed at implementing algorithms and modelling methods will be central to the practical modules and the research project.

Students can expect to receive reports at least termly on the Cambridge Graduate Supervision Reporting System. They will receive comments on items of coursework, and will have access to a University supervisor for their dissertation. All students will also have personal access to the Course Director and the other staff delivering the course.

Assessment

Students will write a dissertation of no more than 15,000 words. An oral presentation will be compulsory, and will contribute to the assessment of the dissertation.

Several of the core courses are examined wholly or mainly through coursework. Some elective options will also be examined through coursework.

Several of the core courses are examined wholly or mainly through written examination. Some of the elective options will also be examined through written examination.

At the discretion of the Examiners, candidates may be required to take an additional oral examination on the work submitted during the course, and on the general field of knowledge within which it falls.

Continuing

Students wishing to apply for continuation to the PhD would normally be expected to attain an overall mark of 70%.

How to apply: http://www.graduate.study.cam.ac.uk/applying

Funding Opportunities

There are no specific funding opportunities advertised for this course. For information on more general funding opportunities, please follow the link below.

General Funding Opportunities http://www.graduate.study.cam.ac.uk/finance/funding

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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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Machine learning, data mining and high-performance computing are concerned with the automated analysis of large-scale data by computer, in order to extract the useful knowledge hidden in it. Read more
Machine learning, data mining and high-performance computing are concerned with the automated analysis of large-scale data by computer, in order to extract the useful knowledge hidden in it. Using state-of-the-art artificial intelligence methods, this technology builds computer systems capable of learning from past experience, allowing them to adapt to new tasks, predict future developments, and provide intelligent decision support. Bristol's recent investment in the BlueCrystal supercomputer - and our Exabyte University research theme - show our commitment to research at the cutting edge in this area.

This programme is aimed at giving you a solid grounding in machine learning, data mining and high-performance computing technology, and will equip you with the skills necessary to construct and apply these tools and techniques to the solution of complex scientific and business problems.

Programme structure

Your course will cover the following core subjects:
-Introduction to Machine Learning
-Research Skills
-Statistical Pattern Recognition
-Uncertainty Modelling for Intelligent Systems

Depending on previous experience or preference, you are then able to take optional units which typically include:
-Artificial Intelligence with Logic Programming
-Bio-inspired Artificial Intelligence
-Cloud Computing
-Computational Bioinformatics
-Computational Genomics and Bioinformatics Algorithms
-Computational Neuroscience
-High Performance Computing
-Image Processing and Computer Vision
-Robotics Systems
-Server Software
-Web Technologies

You must then complete a project that involves researching, planning and implementing a major piece of work. The project must contain a significant scientific or technical component and will usually involve a software development component. It is usually submitted in September.

This programme is updated on an ongoing basis to keep it at the forefront of the discipline. Please refer to the University's programme catalogue for the latest information on the most up-to-date programme structure.

Careers

Skilled professionals and researchers who are able to apply these technologies to current problems are in high demand in today's job market.

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Data is the driving force behind today's information-based society. There is a rapidly increasing demand for specialists who are able to exploit the new wealth of information in large and complex systems. Read more

Data is the driving force behind today's information-based society. There is a rapidly increasing demand for specialists who are able to exploit the new wealth of information in large and complex systems.

The programme focuses on modern methods from machine learning and database management that use the power of statistics to build efficient models, make reliable predictions and optimal decisions. The programme provides students with unique skills that are among the most valued on the labour market.

The rapid development of information technologies has led to the overwhelming of society with enormous volumes of information generated by large or complex systems. Applications in IT, telecommunications, business, robotics, economics, medicine, and many other fields generate information volumes that challenge professional analysts. Models and algorithms from machine learning, data mining, statistical visualisation, computational statistics and other computer-intensive statistical methods included in the programme are designed to learn from these complex information volumes. These tools are often used to increase the efficiency and productivity of large and complex systems and also to make them smarter and more autonomous. This naturally makes these tools increasingly popular with both governmental agencies and the private sector.

The programme is designed for students who have basic knowledge of mathematics, applied mathematics, statistics and computer science and have a bachelor’s degree in one of these areas, or an engineering degree.

Most of the courses included in the programme provide students with deep theoretical knowledge and practical experience from massive amounts of laboratory work.

Students will be given the opportunity to learn:

  • how to use classification methods to improve a mobile phone’s speech recognition software ability to distinguish vowels in a noisy environment
  • how to improve directed marketing by analysing shopping patterns in supermarkets’ scanner databases
  • how to build a spam filter
  • how to provide early warning of a financial crisis by analysing the frequency of crisis-related words in financial media and internet forums
  • how to estimate the effect that new traffic legislation will have on the number of deaths in road accidents
  • how to use a complex DNA microarray dataset to learn about the determinants of cancer
  • how interactive and dynamic graphics can be used to determine the origin of an olive oil sample.

The programme contains a wide variety of courses that students may choose from. Students willing to complement their studies with courses given at other universities have the possibility to participate in exchange studies during the third term. Our partner programmes were carefully selected in order to cover various methodological perspectives and applied areas.

During the final term of the programme, students receive help in finding a private company or a government institution where they can work towards their thesis. There they can apply their knowledge to a real problem and meet people who use advanced data analytics in practice.



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The Institute for Adaptive and Neural Computation (IANC) is a world-leading institute dedicated to the theoretical and empirical study of adaptive processes in both artificial and biological systems. Read more

The Institute for Adaptive and Neural Computation (IANC) is a world-leading institute dedicated to the theoretical and empirical study of adaptive processes in both artificial and biological systems. We are one of the UK’s largest and most prestigious academic teams in these fields.

We foster world-class interdisciplinary and collaborative research bringing together a range of disciplines.

Our research falls into three areas:

  • machine learning
  • computational neuroscience
  • computational biology

In machine learning we develop probabilistic methods that find patterns and structure in data, and apply them to scientific and technological problems. Applications include areas as diverse as astronomy, health sciences and computing.

In computational neuroscience and neuroinformatics we study how the brain processes information, and analyse and interpret data from neuroscientific experiments

The focus in the computational biology area is to develop computational strategies to store, analyse and model a variety of biological data (from protein measurements to insect behavioural data).

Training and support

You carry out your research within a research group under the guidance of a supervisor. You will be expected to attend seminars and meetings of relevant research groups and may also attend lectures that are relevant to your research topic. Periodic reviews of your progress will be conducted to assist with research planning.

A programme of transferable skills courses facilitates broader professional development in a wide range of topics, from writing and presentation skills to entrepreneurship and career strategies.

The School of Informatics holds a Silver Athena SWAN award, in recognition of our commitment to advance the representation of women in science, mathematics, engineering and technology. The School is deploying a range of strategies to help female staff and students of all stages in their careers and we seek regular feedback from our research community on our performance.

Facilities

The award-winning Informatics Forum is an international research facility for computing and related areas. It houses more than 400 research staff and students, providing office, meeting and social spaces.

It also contains two robotics labs, an instrumented multimedia room, eye-tracking and motion capture systems, and a full recording studio amongst other research facilities. Its spectacular atrium plays host to many events, from industry showcases and student hackathons to major research conferences.

Nearby teaching facilities include computer and teaching labs with more than 250 machines, 24-hour access to IT facilities for students, and comprehensive support provided by dedicated computing staff.

Among our entrepreneurial initiatives is Informatics Ventures, set up in 2008 to support globally ambitious software companies in Scotland and nurture a technology cluster to rival Boston, Pittsburgh, Kyoto and Silicon Valley.

Career opportunities

The research you will undertake at ANC is perfectly suited to a career in academia, where you’ll be able to use your knowledge to advance this important field. Some graduates take their skills into commercial research posts, and find success in creating systems that can be used in everyday applications.



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This course specialises in sophisticated data mining and machine learning techniques, exploiting scalable data management and processing infrastructures, including neurotechnology, bioinformatics, security and human-centered computing. Read more

This course specialises in sophisticated data mining and machine learning techniques, exploiting scalable data management and processing infrastructures, including neurotechnology, bioinformatics, security and human-centered computing.

This taught postgraduate course is aimed at students who may not have studied computing exclusively, but who have studied a considerable amount of computing already.

If you want to become a specialist in a particular area of computing, this course will provide a first crucial step towards that goal.

Further information

For full information on this course, including how to apply, see: http://www.imperial.ac.uk/study/pg/computing/machine-learning/

If you have any enquiries you can contact our team at:



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How can we model human task performance? How do our brains combine visual and auditory information? What is the ideal interface for a tired air traffic controller?. Read more
How can we model human task performance? How do our brains combine visual and auditory information? What is the ideal interface for a tired air traffic controller?

The Master's degree programme in Human-Machine Communication (HMC) focuses on Cognitive Science and its applications. By knowing more about cognition you can for example improve the communication between humans and complex computer systems, use language and speech technology or develop simulation models of users. HMC provides you with insights into human cognition and teaches you to use this knowledge in applied settings, such as tutoring systems, speech technology and human-computer interaction.

Two questions form the basis for this programme:
* How does human cognition work?
* How can we use this knowledge in applied settings?

To answer these questions, you have to know how humans perform specific tasks and how this performance limits and affect task performance. How do humans acquire new skills and how do they learn to adapt to a new task? It is important to know in which way information is transferred; the most natural way of human communication is language. But what about machines? Maybe keyboards, choice menus or multi-touch are more suitable.

This programme is unique in combining:
*Artificial Intelligence
*Cognitive Psychology
*Language & Speech Technology

Why in Groningen?

This programme is unique in its combination of:
- Artificial intelligence
- Cognitive psychology
- Language & speech technology

Job perspectives

Once you have obtained your Master's degree, you can then use your skills in industrial research & development departments or usability labs, where you make sure that knowledge about human thinking and acting is used as early as possible in the design process.

In companies where computer software and new media applications are made, user interface design experts, usability testers and interaction designers are needed. If you have specialized in the field of speech and language technology, you can for instance get a job at a telecommunication company.

You could also choose to get a job at a research institute where you work as a researcher. This can be done at a university (PhD studentship) or at a research institute like TNO. About 50% of our students choose a career as a scientist, mostly as PhD student.

Job examples

- Work for a telecommunication company
- Interaction designer
- Usability tester
- PhD research position

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The MSc Machine Intelligence aims to equip students and engineering professionals through a diverse range of research informed learning, with the skills to maintain a future-thinking career. Read more

The MSc Machine Intelligence aims to equip students and engineering professionals through a diverse range of research informed learning, with the skills to maintain a future-thinking career. The programme goes beyond current technology, looking at predicting future innovation by equipping learners with the tools to see through media hype and effectively analyse the evolution of future technologies and engage with these technologies as they emerge.

Whether you're looking to deepen and diversify your industrial experience or continue your education through an innovative Master's degree, this programme provides an ideal opportunity to develop your technical and intellectual skills, staying one step ahead. 

The programme provides the opportunity to explore: future technologies; robotics; cybernetics and intelligent systems; distributed systems; advanced design and ergonomics; securing future technologies; and future business thinking, all set within a forward-thinking context. This programme offers the opportunity to participate in a highly motivated intellectual environment with research-active tutors and like-minded peers, whilst exploring and engaging with cutting-edge future technologies.

Outcomes

The aims of the programme are to:

  • Show you how to analyse, design, implement and manage intelligent and future focused technologies and systems in the context of engineering-related issues facing global societies
  • Provide you with the skills to further your career in these areas
  • Support you in understanding the innovative and pioneering approaches in this field and to be able to apply them to the solution of present, near future and future real-world problems in developing novel industrial and commercially-relevant solutions.

Course content

  • Future Technologies
  • Robotics
  • Cybernetics and Intelligent Systems
  • Distributed Systems
  • Research, Planning and Communication
  • Future Business Thinking
  • Securing Future Technologies
  • Individual Research Project.

Assessment

Assessments include examinations, coursework, group work and an individual project.

Careers

Postgraduate students from this programme will find employment opportunities as futurologists, engineers, scientists and technical managers in the private sector (engineering design firms, engineering consultancy, communications companies, social media companies and similar), in the public sector (local government, town and country planning), an entrepreneur or they may wish to pursue further qualifications such as a PhD within the Faculty of Engineering and Science at the University of Greenwich to become even more specialised. City banks, currency and stocks trading companies, consultancies, government agencies and NGOs will also be interested in employing the type of future orientated intelligent systems engineers that will graduate from this MSc.

Specialised equipment

Online resources: Students will require access to existing online resources such as Moodle, e-mails, library online resources, databases, Web of Knowledge, Scopus, Internet of Things, Internet of Everything.

Hardware: Computers, laboratory equipment to include but not limited to: experimental and laboratory equipment to support practical based learning (hardware and software development systems, robotic hardware, mobile robots, cybernetics hardware and software, Internet of Things, Sensors and Systems, WiFi development, 3D printers, laser cutters, 3D scanners, cutting edge single board computers)

Software: Matlab, Simulink, C/C++ compilers, development systems, networking and communication protocol monitoring and development.

Robotics: A specialist robotics laboratory has been developed containing a Robothespian industrial robotic arm, mobile robots and other robotic actuators and systems.



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