Artificial Intelligence MSc

2019-20 (also available for 2020-21)

This course is eligible for Master's loan funding. Find out more.

Start date

16 September 2019

Duration

1 year full-time

Places available (subject to change)

20

Phone contact: +44 (0)1484 473116

About the course

This course in Artificial Intelligence (AI) is designed to meet the demand for a new kind of IT specialist with skills and knowledge in intelligent systems.

Recent developments in machine learning and the success of high profile AI applications have resulted in governments globally promoting the area as a central component of future technologies in many application areas. This course will aim to develop your knowledge and understanding to an advanced level across a range of areas including:

  • autonomous systems
  • knowledge representation and reasoning
  • data mining
  • machine learning
  • robotics

Graduates of this course will be equipped with an understanding of the fundamental approaches to implementing intelligent behaviour in machines. You will be able to match applications with appropriate AI techniques for their solution, and be able to construct and configure solutions using a range of AI technologies.

The course aims to enhance the technical effectiveness of recent graduates to industry specifically in the area of artificial intelligence.

Methods and techniques to embed machines with intelligent behaviour such as planning, understanding, problem solving and learning are now beginning to mature. Soon intelligent machines will be part of our everyday lives. This course aims to equip students with the foundational knowledge to understand these developments, and with the abilities to enable them to make a contribution to the Artificial Intelligence of the future.

Prof Lee McCluskey

Prof Lee McCluskey, Professor of Software Technology

Course detail

Effective Research and Professional Practice

This module aims to provide you with skills that are key to helping you become a successful computing researcher or practitioner. You'll get the opportunity to study topics including the nature of research, the scientific method, research methods, literature review and referencing. The module aims to cover the structure of research papers and project reports, reviewing research papers, ethical issues (including plagiarism), defining projects, project management, writing project reports and making presentations.

Autonomous and Autonomic Intelligent Systems

Autonomous systems are intelligent systems that can act independently to accomplish goals based on their knowledge and understanding of their environment and the tasks they have to complete. This module aims to cover the background and requirements for intelligent systems autonomy in a wide range of applications, taken from a computer science and software-oriented viewpoint. As well as the technical challenges of system autonomy, you’ll get the opportunity to study ethical and legal issues, and human factors implications.

Data Mining

Data mining is a collection of tools, methods and statistical techniques for exploring and extracting meaningful information from large data sets. It is a rapidly growing field due to the increasing quantity of data gathered by organisations. There is a potential high value in discovering the patterns contained within such data collections. This module looks at different data mining techniques and gives students the chance to use appropriate data-mining tools in order to evaluate the quality of the discovered knowledge. Topics studied include looking at the value of data; approaches to preparing data for exploration; supervised and un-supervised approaches to data mining; exploring unstructured data; social impact of data mining. Current application areas and research topics in data mining will also be discussed and students will be expected to develop their knowledge such that they are able to contribute to such discussions and to increase their background knowledge and understanding of issues and developments associated with data mining.

Knowledge Representation and Reasoning

Knowledge representation and reasoning (KR) is the field of artificial intelligence dedicated to representing information about the world in a form that computer systems can manipulate and utilize to solve complex tasks such as making decisions, diagnosing a medical condition, finding suitable answers to queries or having a dialog in a natural language. Specific KR languages have been developed to express representations. Once information representations are established, reasoning algorithms can be applied to draw conclusions from the available information in a traceable, explainable way. Each KR language is supported by such reasoning algorithms. KR is at the heart of the area of the semantic web, and has found deployment in big corporations such as Google and Amazon in the form of knowledge graphs. This module will enable learners to familiarize themselves with principles and algorithms of knowledge representation and reasoning, and gain experience in using them to solve practical problems.

Machine Learning

Machine Learning techniques are now used widely in a range of applications either stand-alone or integrated with other AI techniques. The Machine Learning module allows you to obtain a fundamental understanding of the subject as a whole: how to embody machines with the ability to learn how to recognise, classify, decide, plan, revise, optimise etc. You will learn which machine learning techniques are appropriate for which learning problem, and what the advantages and disadvantages are for a range of ML techniques. We will consider the widely known data-driven approaches, and specific techniques such as “deep learning”, and investigate the typical applications and potential limitations of these approaches. We will introduce available tools and use them in practical classes, evaluating learning bias and characteristics of training sets. High profile applications of data driven, stand-alone, ML systems will be investigated, such as the AlphaGo method. Where data is sparse, and knowledge is already present in a system, we will investigate methods to improve heuristics of existing AI systems, and to learn or revise domain knowledge. This is essentially the area of model-driven ML, where the learning system is often integrated to other reasoning systems.

Case Studies in Data Analytics and Artificial Intelligence

The purpose of this module is to enable students to appreciate the historical, current and future application areas of AI and DA in relation to both theoretical and practical aspects and to investigate at least one application area in depth. Case studies discussed in the sessions will provide an exploration of applications in a variety of different areas and will be achieved by combinations of study of current research papers, tutors’ own research & the investigative work of the students within the module.

Robotics

The Robotics module allows you to gain specialist knowledge in robotic devices and autonomous applications by examining the integration of mechanical devices, sensors and ‘intelligent’ computerised robotic agents. You will also explore the latest developments in robotics and intelligent systems through a series of investigative tasks and practical sessions. The module covers essential techniques for the design an development of robotic based systems using a collection of robotic hardware and simulation software. It supports the discussion and analysis of the hardware and software used to build real-world robotic systems. It introduces device and architectural specific topics required to enable students to design and develop software for intelligent autonomous robots. This will include low-level programming of I/O devices for robotic swarms, sensor systems and active modelling and simulation. It will introduce planning for intelligent robots taking a lifecycle approach from theory to activation.

Artificial Intelligence Planning

This module will recap on the history of automated planning from the days of STRIPS, up to the present day. It will focus on the kinds of assumptions, algorithms, heuristics and representation languages that have been used to create generative planning algorithms. It will illustrate these developments using a range of planning engines and planning platforms. Current application areas and research topics in automated planning, such as hybrid planning, will be discussed and students will be expected to develop their knowledge such that they are able to contribute to such discussions and to increase their background knowledge and understanding of issues and developments associated with AI Planning.

Individual Project

This module enables the student to work independently on a project related to a self-selected problem. A key feature in this final stage of the MSc is that students will be encouraged to undertake an in-company project with an external Client. Where appropriate, however, the Project may be undertaken with an internal Client - research-active staff - on larger research and knowledge transfer projects. The Project is intended to be integrative, a culmination of knowledge, skills, competencies and experiences acquired in other modules, coupled with further development of these assets. In the case where an external client is involved, both the Client and Student will be required to sign a learning agreement that clearly outlines scope, responsibilities and ownership of the project and its products or other deliverables. The Project will be student-driven, with the clear onus on the student to negotiate agreement, and communicate effectively, with all parties involved at each stage of the Project.

Entry requirements

Entry requirements for this course are normally:

  • A BSc or BEng Honours degree (2:2 or above) in Computing or Engineering or scientific related subject or an equivalent professional qualification
  • Applicants are expected to be familiar with and have some aptitude for Discrete Mathematics and Predicate Calculus
  • Other qualifications and/or experience that demonstrate appropriate knowledge and skills at an Honours degree level - the qualification and experience should be in the area of Computer Science or Mathematics/Engineering

If your first language is not English, you will need to meet the minimum requirements of an English Language qualification. The minimum of IELTS 6.5 overall with 6.0 in Writing and no element lower than 5.5, or equivalent will be considered acceptable.

Why Choose Huddersfield?


Watch this clip to find out five great reasons to choose the University of Huddersfield for postgraduate study.

Teaching excellence

  1. Huddersfield is a TEF gold-rated institution delivering consistently outstanding teaching and learning of the highest quality found in the UK (Teaching Excellence Framework, 2017).
  2. We won the first Global Teaching Excellence Award recognising the University’s commitment to world-class teaching and its success in developing students as independent learners and critical thinkers (HEA, 2017).
  3. Here at Huddersfield, you’ll be taught by some of the best lecturers in the country. We’ve been the English university with the highest proportion of professionally-qualified teaching staff for the past four years*.
  4. For the past ten years, we’ve been the UK’s leading university for National Teaching Fellowships too, which rate Britain’s best lecturers. It’s all part of our ongoing drive for teaching excellence, which helps our students to achieve great things too.
  5. We’re unique in the fact that all our permanent teaching staff** have, or are completing, doctorates. This expertise, together with our teaching credentials, means that students here learn from knowledgeable and well-qualified teachers and academics who are at the forefront of their subject area.

*HESA - First awarded in 2016, maintained in 2017, 2018 and 2019.

**Permanent staff, after probation: some recently appointed colleagues will only obtain recognition in the months after their arrival in Huddersfield, once they have started teaching; research degrees applies to those on contracts of more than half-time.

Enhance your career


* Percentage of our postgraduate students who go on to work and/or further study within six months of graduating (Destination of Leavers from Higher Education Survey 2016/17).

93.8%*

Student support

At the University of Huddersfield, you'll find support networks and services to help you get ahead in your studies and social life. Whether you study at undergraduate or postgraduate level, you'll soon discover that you're never far away from our dedicated staff and resources to help you to navigate through your personal student journey. Find out more about all our support services.

In the School of Computing and Engineering we have a dedicated guidance team that provides the students that need it, guidance and support on both academic and non-curriculum matters.These may include:

  • Settling in
  • Personal development
  • Health and wellbeing
  • Balancing work and studies
  • Exam and assignment preparation
  • Staying the course (attendance, change course, extensions etc.)
  • Study skills and Technical English support from our Academic Skills Tutor

Important information

We will always try to deliver your course as described on this web page. However, sometimes we may have to make changes as set out below.

Changes to a course you have applied for

If we propose to make a major change to a course that you are holding an offer for, then we will tell you as soon as possible so that you can decide whether to withdraw your application prior to enrolment.

Changes to your course after you enrol as a student

We will always try to deliver your course and other services as described. However, sometimes we may have to make changes as set out below:

Changes to option modules

Where your course allows you to choose modules from a range of options, we will review these each year and change them to reflect the expertise of our staff, current trends in research and as a result of student feedback or demand for certain modules. We will always ensure that you have a range of options to choose from and we will let you know in good time the options available for you to choose for the following year.

Major changes

We will only make major changes to the core curriculum of a course or to our services if it is necessary for us to do so and provided such changes are reasonable. A major change in this context is a change that materially changes the services available to you; or the outcomes, or a significant part, of your course, such as the nature of the award or a substantial change to module content, teaching days (part time provision), classes, type of delivery or assessment of the core curriculum.

For example, it may be necessary to make a major change to reflect changes in the law or the requirements of the University’s regulators; to meet the latest requirements of a commissioning or accrediting body; to improve the quality of educational provision; in response to student, examiners’ or other course evaluators’ feedback; and/or to reflect academic or professional changes within subject areas. Major changes may also be necessary because of circumstances outside our reasonable control, such as a key member of staff leaving the University or being unable to teach, where they have a particular specialism that can’t be adequately covered by other members of staff; or due to damage or interruption to buildings, facilities or equipment.

Major changes would usually be made with effect from the next academic year, but this may not always be the case. We will notify you as soon as possible should we need to make a major change and will carry out suitable consultation with affected students. If you reasonably believe that the proposed change will cause you detriment or hardship we will, if appropriate, work with you to try to reduce the adverse effect on you or find an appropriate solution. Where an appropriate solution cannot be found and you contact us in writing before the change takes effect you can cancel your registration and withdraw from the University without liability to the University for future tuition fees. We will provide reasonable support to assist you with transferring to another university if you wish to do so.

Termination of course

In exceptional circumstances, we may, for reasons outside of our control, be forced to discontinue or suspend your course. Where this is the case, a formal exit strategy will be followed and we will notify you as soon as possible about what your options are, which may include transferring to a suitable replacement course for which you are qualified, being provided with individual teaching to complete the award for which you were registered, or claiming an interim award and exiting the University. If you do not wish to take up any of the options that are made available to you, then you can cancel your registration and withdraw from the course without liability to the University for future tuition fees and you will be entitled to a refund of all course fees paid to date. We will provide reasonable support to assist you with transferring to another university if you wish to do so.

When you enrol as a student of the University, your study and time with us will be governed by a framework of regulations, policies and procedures, which form the basis of your agreement with us. These include regulations regarding the assessment of your course, academic integrity, your conduct (including attendance) and disciplinary procedure, fees and finance and compliance with visa requirements (where relevant). It is important that you familiarise yourself with these as you will be asked to agree to abide by them when you join us as a student. You will find a guide to the key terms here, where you will also find links to the full text of each of the regulations, policies and procedures referred to.

The Office for Students (OfS) is the principal regulator for the University.

You may also be interested in...

Information Systems Management MSc

You’ll learn key skills in databases, design, and system operation. With an engaging and challenging course, you’ll be fully prepared for a career in IT.

Find out more How to apply

Full-time

Postgraduate


Internet of Things MSc

The Internet of Things (IoT) is a new and rapidly expanding area in Computer Science, technology and engineering.

Find out more How to apply

Full-time

Postgraduate