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Artificial Intelligence (with Placement) MSc

2023-24 (also available for 2022-23)

Start date

25 September 2023

8 January 2024

Duration

18 months with placement

Places available (subject to change)

20

About the course

Reasons to study

  1. We’ll support you in finding a 6 month work placement. You could work in industry, a research group or within a teaching environment.
  2. We'll equip you with the knowledge, underpinning recent AI developments as well as the skills to enable you to contribute to future AI technologies.
  3. You'll have access to industry standard hardware and software to help you prepare for your future career.

This course is specifically aimed at International students, supporting those wishing to gain practical work based experiential learning. Together with studying your chosen course we are offering you the opportunity to secure a placement* for an additional 6 months, making the course 18 months in length. This allows students with limited experience to put into practice the skills and techniques developed throughout the Master’s degree.

Increased use of Artificial Intelligence (AI) can bring major social and economic benefits to the UK. Skilled experts are needed to develop AI and they are in short supply. To develop more AI, the UK will need a larger workforce with deeper AI expertise.*

This course 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

Our aim is to equip you with an understanding of the fundamental approaches to implementing intelligent behaviour in machines. This should then enable you to match applications with appropriate AI techniques for their solution, and be able to construct and configure solutions using a range of AI technologies.

gov.uk, 2017, Growing the artificial intelligence industry in the UK. Please see Placement Section for more details about this.

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 utilise 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. This module will introduce you to KR principles, languages and algorithms and help you gain experience in using them to solve practical problems. You will also learn about applications such as the semantic web and knowledge graphs which have found deployment in big corporations such as Google and Amazon.

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 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 you to work independently on a project related to a self-selected problem. A key feature in this final stage of the course is that you 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 you to negotiate agreement, and communicate effectively, with all parties involved at each stage of the Project.

Professional Development and Practice

This module provides students with the opportunity to reflect on their professional practice during their Masters degree by undertaking a period of development through study or by working with a company, research group or within a teaching environment in the UK or overseas. This module encourages students to reflect on their technical, personal and professional development experiences, and to identify their learning from these experiences.

Entry requirements

Entry requirements for this course are normally:

  • A BSc or BEng Honours degree (2:1 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 for IELTS is 7.0 overall with no element lower than 6.5, or equivalent. Read more about the University’s entry requirements for students outside of the UK on our Where are you from information pages.

Why Huddersfield?


Find out why some of our students chose to study with us. From feeling at home as soon as they came to campus, to the fantastic facilities, friendly community and engaging courses.

Placements


This course provides you with the chance to undertake a 6 month placement at the end of the taught element. This may be by working in a company, a research group or within a teaching environment. A placement can help you build on the knowledge and skills developed on the course.

The placement is a valuable tool that can enhance your employability and help you to develop as an individual. It is acknowledged that graduates with work experience are generally much more attractive to employers.

Our Placement Unit will be on hand to support you in finding a suitable placement opportunity. Students will have access to online learning materials to pick up advice on CVs, cover letters, speculative applications, online applications, and interviews. In addition, students will have access to the placement drop-in sessions for one-to-one advice and have a mock interview.

Enhance your career


We would expect to see graduates progress to careers as Robotics Programmer, Machine Learning Engineer, Data Mining Analyst or Software Engineer for example. You could also go on to further study and the University has many options available for postgraduate research which may interest you.

*  Percentage of graduates from the School of Computing and Engineering who are in work and/or further study fifteen months after graduating (HESA Graduate Outcomes 18/19, UK domiciled graduates)

94.1%*

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.

A wide range of resources are also offered within the School of Computing and Engineering, which provides you with support in a variety of areas. These include:

Student Support Office: a one stop shop for students studying within the School. The team deal with every aspect of student life from enrolment, module queries, timetabling, exams, assessments, course-related committees and graduation. They are the first place to go with any query, and they can also signpost to other support networks.

Student Guidance Office: provides guidance about how students can develop their academic study skills and learning development. The team provide support with academic skills including research and project planning, referencing and paraphrasing, essay writing, critical thinking, understanding assessments and the presentation of academic work. Common learning development topics include, developing effective study habits, time management, how to manage deadlines, plan, structure and organise work and understanding the University regulations and systems.

Technical Support: technicians support our students across each department. Based in our labs with different specialisms and knowledge they are on hand to advise and guide, students can access our technician’s expertise during lectures and seminars as well as during self-study. A technical Helpdesk is also available to all students within the School of Computing and Engineering to help troubleshoot any computer issues or to borrow hardware and software.

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, along with the Student Protection Plan, 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.

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