The Intelligent Systems MSc degree course is designed to give graduates the understanding, practical knowledge and expertise to evaluate, design and build intelligent systems using an extensive range of tools and techniques.
The Intelligent Systems MSc will prepare you for work developing intelligent control and engineering systems. You will study Artificial Intelligence, Agents and Multi-agent Systems, Pattern Recognition, Computer Vision and Biologically Inspired Methods. There are also opportunities to explore a broad range of optional modules allowing you the freedom to develop your study pathway to reflect your interests.
You will complete the course in one year, studying September to September and taking a combination of required and optional modules totalling 180 credits, including 60 credits that will come from an individual project of 15,000 words.
For graduates in engineering, computing or a related scientific discipline, with a good knowledge of computer programming and mathematics, from this programme you will gain specialist training in designing, building and evaluating intelligent systems using a range of tools and techniques in preparation for a career in research or industry.
Teaching
We use lectures, seminars and group tutorials to deliver most of the modules on the programme. You will also be expected to undertake a significant amount of independent study.
Assessment
The primary method of assessment for this course is a combination of written examinations, essays, coursework and individual or group projects and oral presentations. The research project will be assessed through a dissertation.
Our graduates have gone on to pursue successful careers in industry, commerce and academia.
From software agents used in networking systems to embedded systems in unmanned vehicles, intelligent systems are being adopted more and more often. This programme will equip you with specialist knowledge in this exciting field and allow you to explore a range of topics in computer science.
Core modules will give you a foundation in topics like systems programming and algorithms, as well as the basics of machine learning and knowledge representation. You’ll also choose from optional modules focusing on topics like bio-inspired computing or text analytics, or broaden your approach with topics like mobile app development.
You’ll gain a broad perspective on intelligent systems, covering evolutionary models, statistical and symbolic machine learning algorithms, qualitative reasoning, image processing, language understanding and bio-computation as well as essential principles and practices in the design, implementation and usability of intelligent systems.
Specialist facilities
You’ll benefit from world-class facilities to support your learning. State-of-the-art visualisation labs including a powerwall, a benchtop display with tracking system, WorldViz PPT optical tracking system and Intersense InertiaCube orientation tracker are all among the specialist facilities we have within the School of Computing.
We also have Ascension Flock of Birds tracking systems, three DOF and 6DOF Phantom force feedback devices, Twin Immersion Corp CyberGloves, a cloud computing testbed, rendering cluster and labs containing both Microsoft and Linux platforms among others. It’s an exciting environment in which to gain a range of skills and experience cutting-edge technology.
Taught by experienced research staff from the Centre for Computational Intelligence (CCI), an internationally recognised centre highly rated in the most recent Government Research Assessment Exercise, you gain a professional qualification that gives substantially enhanced career and research prospects in both traditional computing areas and in the expanding area of computational intelligence.
Computational Intelligence encompasses the techniques and methods used to tackle problems not well solved by traditional approaches to computing. The four areas of fuzzy logic, neural networks, evolutionary computing and knowledge based systems encompass much of what is considered to be computational (or artificial) intelligence. There are opportunities to use these techniques in many application areas such as robot control and games development depending on your interests.
Modules include work based on research by the Centre of Computational Intelligence. With an established international reputation, their work focuses on the use of fuzzy logic, artificial neural networks, evolutionary computing, mobile robotics and biomedical informatics, providing theoretically sound solutions to real-world decision making and prediction problems. Past students have published papers with their CCI project supervisors and gone on to PhD study.
• Internationally recognised reputation
our internationally recognised Centre of Computational Intelligence (CCI) inputs into the course allowing you to understand the current research issues related to artificial intelligence
• Benefit from our Research Expertise
modules include work-based on research by our Centre for Computational Intelligence (CCI) and focus on the use of fuzzy logic, artificial neural networks, evolutionary computing, mobile robotics and biomedical informatics; providing theoretically sound solutions to real-world decision making and prediction problems
• Flexible study options
full-time, part time or distance learning study options available; making the course suitable for recent graduates and professionals in work
• Dedicated robotics laboratory
have access to our Advanced Mobile Robotics and Intelligent Agents Laboratory. The laboratory contains a variety of mobile robots ranging from the Lego Mindstorms and Pioneers to the Wheelbarrow robot for bomb disposal
• Employment Prospects
artificial Intelligence is a growing industry worldwide, employment opportunities exist in areas such as games development, control systems, software engineering, internet businesses, financial services, mobile communications, programming, and software engineering
First semester
• Research Methods
• Artificial Intelligence Programming
• Mobile Robots
• Fuzzy Logic
Second semester
• Artificial Neural Networks
• Computational Intelligence Optimisation (CIO)
• Applied Computational Intelligence
• Data Mining, Techniques and Applications
(Intelligence Systems only)
• Intelligent Mobile Robots (Intelligent Systems
and Robotics only)
Third semester
• Individual Project
We offer a great opportunity to boost your career prospects through an optional one year placement as part of your postgraduate studies. We have a dedicated Placement Unit which will help you obtain this. Once on your placement you will be supported by your Visiting Tutor to ensure that you gain maximum benefit from the experience. Placements begin after the taught component of the course has been completed - usually around June - and last for one year. When you return from your work placement you will begin your project.
The course consists of an induction unit, eight modules and an individual project. The summer period is devoted to work on the project for full-time students. If you choose to study via distance learning, you would normally take either one module per semester for four years or two modules per semester for four years plus a further year for the project.
Teaching is normally delivered through lectures, seminars, tutorials, workshops, discussions and e-learning packages. Assessment is via coursework only and will usually involve a combination of individual and group work, presentations, essays, reports and projects.
Distance learning material is delivered primarily through our virtual learning environment. Books, DVDs and other learning materials will be sent to you. We aim to replicate the on-site experience as fully as possible by using electronic discussion groups, encouraging contact with tutors through a variety of mediums.
On-site students will have the lessons delivered by the module tutors in slots of three hours. In the full-time route, you can expect to have around 12 hours of timetabled taught sessions each week, with approximately 28 additional hours of independent study. There are also three non-teaching weeks when fulltime students can expect to spend around 40 hours on independent study each week.
To learn more about this course and DMU, visit our website:
Postgraduate open days: http://www.dmu.ac.uk/study/postgraduate-study/open-evenings/postgraduate-open-days.aspx
Applying for a postgraduate course:
Funding for postgraduate students
Taught by experienced research staff from the Centre for Computational Intelligence (CCI), an internationally recognised centre highly rated in the most recent Government Research Assessment Exercise, you gain a professional qualification that gives substantially enhanced career and research prospects in both traditional computing areas and in the expanding area of computational intelligence.
Computational Intelligence encompasses the techniques and methods used to tackle problems not well solved by traditional approaches to computing. The four areas of fuzzy logic, neural networks, evolutionary computing and knowledge based systems encompass much of what is considered to be computational (or artificial) intelligence. There are opportunities to use these techniques in many application areas such as robot control and games development depending on your interests.
Modules include work based on research by the Centre of Computational Intelligence. With an established international reputation, their work focuses on the use of fuzzy logic, artificial neural networks, evolutionary computing, mobile robotics and biomedical informatics, providing theoretically sound solutions to real-world decision making and prediction problems. Past students have published papers with their CCI project supervisors and gone on to PhD study.
• Internationally recognised reputation
our internationally recognised Centre of Computational Intelligence (CCI) inputs into the course allowing you to understand the current research issues related to artificial intelligence
• Benefit from our Research Expertise
modules include work-based on research by our Centre for Computational Intelligence (CCI) and focus on the use of fuzzy logic, artificial neural networks, evolutionary computing, mobile robotics and biomedical informatics; providing theoretically sound solutions to real-world decision making and prediction problems
• Flexible study options
full-time, part time or distance learning study options available; making the course suitable for recent graduates and professionals in work
• Dedicated robotics laboratory
have access to our Advanced Mobile Robotics and Intelligent Agents Laboratory. The laboratory contains a variety of mobile robots ranging from the Lego Mindstorms and Pioneers to the Wheelbarrow robot for bomb disposal
• Employment Prospects
artificial Intelligence is a growing industry worldwide, employment opportunities exist in areas such as games development, control systems, software engineering, internet businesses, financial services, mobile communications, programming, and software engineering
First semester
• Research Methods
• Artificial Intelligence Programming
• Mobile Robots
• Fuzzy Logic
Second semester
• Artificial Neural Networks
• Computational Intelligence Optimisation (CIO)
• Applied Computational Intelligence
• Data Mining, Techniques and Applications
(Intelligence Systems only)
• Intelligent Mobile Robots (Intelligent Systems
and Robotics only)
Third semester
• Individual Project
We offer a great opportunity to boost your career prospects through an optional one year placement as part of your postgraduate studies. We have a dedicated Placement Unit which will help you obtain this. Once on your placement you will be supported by your Visiting Tutor to ensure that you gain maximum benefit from the experience. Placements begin after the taught component of the course has been completed - usually around June - and last for one year. When you return from your work placement you will begin your project.
The course consists of an induction unit, eight modules and an individual project. The summer period is devoted to work on the project for full-time students. If you choose to study via distance learning, you would normally take either one module per semester for four years or two modules per semester for four years plus a further year for the project.
Teaching is normally delivered through lectures, seminars, tutorials, workshops, discussions and e-learning packages. Assessment is via coursework only and will usually involve a combination of individual and group work, presentations, essays, reports and projects.
Distance learning material is delivered primarily through our virtual learning environment. Books, DVDs and other learning materials will be sent to you. We aim to replicate the on-site experience as fully as possible by using electronic discussion groups, encouraging contact with tutors through a variety of mediums.
On-site students will have the lessons delivered by the module tutors in slots of three hours. In the full-time route, you can expect to have around 12 hours of timetabled taught sessions each week, with approximately 28 additional hours of independent study. There are also three non-teaching weeks when fulltime students can expect to spend around 40 hours on independent study each week.
Taught by experienced research staff from the Centre for Computational Intelligence (CCI), an internationally recognised centre highly rated in the most recent Government Research Assessment Exercise, you will gain a professional qualification that gives substantially enhanced career and research prospects in both traditional computing areas and in the expanding area of computational intelligence.
To learn more about this course and DMU, visit our website:
Postgraduate open days: http://www.dmu.ac.uk/study/postgraduate-study/open-evenings/postgraduate-open-days.aspx
Applying for a postgraduate course:
Funding for postgraduate students
This international Master’s programme gives insights into cutting edge resarch in robotics and artificial intelligence. The programme is centred around a "raise your robot" theme. You get access to a robot platform from day one, which you will give more and more advanced capabilities as the courses are progressing.
The programme is offered by the nationally and internationally renowned Centre for Applied Autonomous Sensor Systems (AASS) at Örebro University. As a student, you will be immersed in one of Sweden's largest academic research centres in robotics and intelligent systems. The outcomes of our ongoing international research projects feed into all of the courses. Vice versa, student projects and thesis works typically contribute to our research projects with industry partners.
This MSc focuses on state-of-the-art technologies for autonomous control and machine learning with applications in robotics, sensor networks, big data analytics, and autonomous agents.
Emphasis is given to topics that support a new emerging generation of self-sustaining and intelligent devices created for the Internet of Things, Ubiquitous Computing, and Industry 4.0 environments.
This degree is designed to provide a wide-ranging background in autonomous technologies that can be applied in a variety of disciplines.
In addition to traditional technologies related to robotics, embedded systems, design, and control, students will be exposed to system-level design methods and state-of-the-art theory behind some of the newest and most promising fields of artificial intelligence.
Applications include autonomous mobile systems, digital manufacturing, Big Data analytics, Internet of things device engineering, and artificial intelligence programming.
The MSc is delivered jointly by the Departments of Electronic & Electrical Engineering, and Design, Manufacture & Engineering Management.
This ensures you have access to academic leaders in the fields of machine learning, autonomous systems, digital manufacturing and design engineering.
You’ll take two semesters of compulsory and optional taught classes. These are followed by a three-month research project in your chosen area. Opportunities exist to do the project through the departments' competitive MSc industrial internships.
The internships are offered in collaboration with selected department industry partners. You’ll address real-world engineering challenges facing the partner, with site visits, access and provision of relevant technical data and/or facilities provided, along with an industry mentor and academic supervisor.
We’ve a wide range of excellent teaching spaces including interactive flexible learning spaces, and state-of-the-art facilities. Our Technology and Innovation Centre (TIC) is home to a number of world class labs where students will have an opportunity to undertake research projects in relevant areas. The University is also home to some key and relevant industry engagement research centres, including:
Interaction with industry is provided through our internships, teaching seminars and networking events. The departments deliver monthly seminars to support students’ learning and career development.
Xilinx, Texas Instruments, MathWorks, and Leonardo are just a few examples of the industry partners you can engage with during your programme of study.
We use a blend of teaching and learning methods including interactive lectures, problem-solving tutorials and practical project-based laboratories.
Our technical and experimental officers are available to support and guide you on individual subject material. Each module comprises approximately five hours of direct teaching per week.
To enhance your understanding of the technical and theoretical topics covered in these, you're expected to undertake a further five to six hours of self-study, using our web-based virtual learning environment (MyPlace), research journals and library facilities.
The teaching and learning methods used ensure you'll develop not only technical engineering expertise but also communications, project management and leadership skills.
A variety of assessment techniques are used throughout the course. Each module has a combination of written assignments, individual and group reports, oral presentations, practical lab work and, where appropriate, an end-of-term exam.
Assessment of the summer research project consists of four elements, with individual criteria:
Job titles include:
Employers include:
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.
The aims of the programme are to:
Assessments include examinations, coursework, group work and an individual project.
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.
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.
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.
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