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Introduction
Why choose this course?
Benefit from teaching by experienced staff from our internationally recognised Institute of Artificial Intelligence that conducts world-leading research into artificial intelligence, computational intelligence and intelligent systems.
Enhance your practical and professional skills with work placement opportunities. Students have undertaken placements at companies such as IBM, Microsoft and PayPoint.
Access specialist facilities, including our Advanced Mobile Robotics and Intelligent Agents Laboratory, which contain a variety of mobile robots.
Put your skills into practice by taking part in the DMU Robot Club, with a chance to compete in an international robotics competition. Our students and staff have previously won prizes at the Robot Challenge in Vienna.
Expand your horizons and enrich your studies with a DMU Global experience. Students on related courses have tested their hacking skills at New York’s Spyscape Museum and networked with tech entrepreneurs in Silicon Valley.
Benefit from Education 2030, where a simplified ‘block learning’ timetable means you will study one subject at a time and have more time to engage with your learning, receive faster feedback and enjoy a better study-life balance.
Overview
The artificial intelligence industry is fast-growing and touches so many aspects of society, from business operations to our everyday lives. On this course you will gain a detailed understanding of artificial intelligence concepts and techniques and learn to use this knowledge to address contemporary problems and challenges such as the use of microprocessor-based systems to control home appliances and the detection and resolution of collisions in applied mechanics.
You will develop fundamental skills, such as learning the powerful general-purpose programming language C++, and experience in-depth study of computer networks and systems. Using artificial intelligence code, you will learn how to control advanced mobile robots in our purpose-built laboratory.
Our modules are designed to focus on real-life industry scenarios to enhance your employability. You will also be able to enjoy projects and extracurriculars related to your course. Get involved with our dedicated Robot Club, where you will solidify your skills by instructing secondary school children on robotic construction or take the opportunity to develop your own robot project, with the potential to compete in the annual international Robot Challenge in Vienna
Modules
Year 1
Block 1: Database Design and Implementation
Structured data, held in relational databases, accessed via SQL, supports the information storage requirements of many companies, organisations, and on-line businesses. In this module the student will learn the fundamentals of how to design the structure of data within a relational database, how to interact with data within the database, and how to protect the data within the database.
The methods of delivery during this block will include workshops used to introduce and demonstrate key practical and theoretical concepts. Practical programming skill will be gained in regular laboratory sessions. Some sessions may be used for consolidation, revision, and to discuss solutions to practical problems.
Workshop: 42 hours
Practical: 20 hours
Seminar: 4 hours
Self-directed study: 76 hours
Consolidation: 68 hours
Reading: 30 hours
Assessment: 60 hours
Block 2: Fundamental Concepts of Computer Science
This module introduces students to fundamental concepts in computer science in relevant areas of mathematics (including propositional logic, set notation, etc); software modelling; the software lifecycle; requirements capture; user interface design; and the foundations of ethical thinking. These topics can then be applied and further developed as students progress throughout the course.
The methods of delivery during this block include workshops used to introduce the main topics. To gain full advantage of this module students will hone their skills and understanding by working through progressive exercises ranging from drill to problem solving tasks. The exercises provide the basis of tutorial seminar and laboratory work. In seminars students receive feedback on their progress and engage in discussions on issues arising from the exercises.
Workshop: 42 hours
Seminar: 24 hours
Self-directed study: 66 hours
Consolidation: 58 hours
Reading: 30 hours
Revision: 20 hours
Assessment: 60 hours
Block 3: Computer Programming
Computer programming requires the analysis of a problem, the production of requirements, and their translation into a design that can be executed on a computer. This module introduces the skills required to develop a computer program to solve a given problem and does so from the perspective of designing trustworthy software with an emphasis on sound coding principles and unit testing.
The methods of delivery during this block will include workshops used to introduce and demonstrate key practical and theoretical concepts. Practical programming skill will be gained in regular laboratory sessions. Some sessions may be used for consolidation, revision, and to discuss solutions to practical problems.
Workshop: 24 hours
Practical: 42 hours
Self-directed study: 76 hours
Consolidation: 68 hours
Reading: 30 hours
Assessment: 60 hours
Block 4: Operating Systems and Networks
This module is designed to provide a foundation in computer architecture, operating systems, and computer networks. Covering theoretical foundations, computer hardware, systems software, computer networks and security issues.
The methods of delivery during this block will include lectures which will be used to introduce the main theoretical elements and laboratory sessions for practical application and experimentation.
Workshop: 24 hours
Practical: 42 hours
Self-directed study: 66 hours
Consolidation: 68 hours
Reading: 40 hours
Assessment: 60 hours
Year 2
Block 1: Computational Intelligence and Computer Systems
Computational Intelligence (CI) is a significant branch of Artificial Intelligence (AI), which uses soft computing and nature-inspired techniques to respond to computationally-difficult problems with accuracy and robustness. Students will cover two of the “pillars” of CI in-depth, neural networks and evolutionary systems, and supplement this with content from the fields of natural computation and natural language processing.
The neural networks content will first give students strong foundations in the subject, to then succeed in the more complex area of deep learning. A knowledge of evolutionary systems will give students tools to describe the solutions to computationally-complex problems and use evolutionary techniques to solve them.
The module will provide an overview of popular natural computation techniques to compliment these two pillars of CI, including ant-colony optimisation, swarm intelligence, and social network graphs. Natural language processing will look at the building blocks of language and semantic understanding, and how to apply CI and natural computation techniques to this field. Finally, the module is grounded in ethical data handling to ensure AI professionals who are able to use data competently and safely.
This block module runs over seven weeks of teaching time with the following delivery pattern:
Workshop: 42 hours
Seminar: 4 hours
Practical: 20 hours
Self-directed study: 76 hours
Assessment: 60 hours
Block 2: Intelligent Robotics
Intelligent robots are becoming commonplace, and the next generation of Artificial Intelligence (AI) professionals will need a good grounding in how robots operate from both physical and programmatic perspectives. This module provides students with a strong foundation in the physicality of robots, covering sensors, computer vision, actuators, stationary robots and robots that must navigate their environment.
Students will learn how to mathematically describe robots’ movement through 2D and 3D space, as well as apply that maths to make their robots build maps and locate themselves in their environment. The module then covers planning and goal-orientated behaviour, so that students can create robots that are able to follow plans and prioritise task-loads in order to complete larger tasks. This is supplemented by an introduction to reinforcement learning, to give students an understanding of how such robots may learn in their environments to improve their behaviours.
Workshops will be used to introduce and demonstrate key practical and theoretical concepts. Practical programming skills will be gained in laboratory sessions. Some sessions may be used for consolidation, revision, and to discuss solutions to practical problems.
Workshop: 42 hours
Practical: 20 hours
Seminar: 4 hours
Self-directed study: 76 hours
Consolidation: 68 hours
Reading: 30 hours
Assessment: 60 hours
Block 3: Applied Artificial Intelligence
The module focuses on introducing the practical applications of AI by giving a tour through AI techniques and algorithms with examples.
Content Outline:
• AI Modelling Techniques
• Knowledge Structures
• Expert and Knowledge-based Systems with examples from Health Applications.
• Autonomous Systems with examples from Industry 4.0 Applications.
• Cognitive Systems with examples from Conversational AI/Bots.
• Swarm Intelligence with examples from Smart Cities and Sustainable Development Applications.
• Advanced AI programming techniques/approaches.
• Human-Centered AI: Responsible AI and Well-being Metrics (this would be the ethical component and build up on the work I have done as part of the working group for the IEEE Standards on Well-Being Metrics for A/IS.)
• Open-source and proprietary tools
The module will cover extensive examples of Applied AI and relate the covered topics to other modules within the programme providing a practical context from industry and day-to-day use of AI.
Lecture: 7 hours
Practical: 48 hours
Self-directed study: 25 hours
Assessment: 220 hours
Block 3: Agile Team Development Project
This module is an opportunity for students to engage in a constrained work-place simulation based on agile software development. Students working in teams of 3 to 5 will initially identify a system of sufficient size to be distributed equally among all members. Each team member might take individual ownership of the development of 2-3 classes from initial inception to completion providing CRUD functionality.
The methods of delivery during this block will include workshops, seminars to introduce and discuss ethical issues, and practical programming skills will be gained in regular laboratory sessions. Some workshops and practical laboratory sessions may be used for consolidation and to discuss solutions to practical and ethical problems.
Workshop: 42 hours
Practical: 20 hours
Seminar: 4 hours
Self-directed study: 76 hours
Consolidation: 78 hours
Reading: 20 hours
Assessment: 60 hours
Year 3
Block 1: Agent-Based Modelling and Parallel Computing
The module will provide a comprehensive introduction to Parallel Programming with application in Agent-based modelling and multi-agent systems programming. The module will cover the following topics:
• Concepts and phenomena in complex systems
• Hardware Trends encouraging parallelism
• Need for explicit parallel programming
• Parallel Programming models
• Strategies and mechanisms for parallel programming
• Existing agent-based modelling software platforms
• Multi-threading with CUDA
• CUDA in Action
• Practical Agent-Based modelling
• Applications of agent-based modelling and multi-agents systems
Lecture: 7 hours
Practical: 48 hours
Self-directed study: 25 hours
Assessment: 220 hours
Block 2: Big Data and Machine Learning
The module will focus on machine learning (ML) and its application to Big Data in a “taster-like” fashion. That is, ML will be applied to solve analytics problems using appropriate tools e.g., Apache Spark that avail ML libraries. As this is done ML algorithms will be introduced and then applied. The focus is therefore not so much on the technical details of the algorithms - rather, the ability to implement them and use them within analytics. The module covers supervised and unsupervised learning techniques with a specific application to data mining.
Lectures will be used to discuss concepts, theories, and applications including machine learning algorithms and data analytics tools. Practical sessions will be used to undertake practical aspects of the module to solve selected data analytics problems from a wide range of areas.
Lecture/Workshop: 24 hours
Seminar: 7 hours
Practical: 35 hours
Self-directed study: 70 hours
Consolidation: 64 hours
Reading: 40 hours
Assessment: 60 hours
Block 3 and 4: Development Project
This project provides students with the opportunity to demonstrate practical and analytical skills present in their programme of study; to work innovatively and creatively; to synthesise information, ideas, and practices to provide a quality solution, together with an evaluation of that solution.
The project is primarily self-directed with guidance and support from an assigned supervisor.
Lecture: 4 hours
Supervisor meetings: 5 hours
Self-directed study: 231 hours
Assessment: 60 hours
Block 3 and 4: Fuzzy Logic and Inference Systems
Fuzzy logic is a mathematical model for handling uncertainty, it is able to provide a means in order to successfully inference from abstract and subjective notions. Fuzzy logic adopts the perspective that the world and humanistic understanding are inherently vague and not precise. Concepts like that of; hot; cold; near; far; and other forms of expressive language where precise values are not given, are extremely difficult to model when universal understanding of such concepts are non-existent.
What is beautiful to some, may not be beautiful to others; concepts can have different meanings to different people. Fuzzy logic and fuzzy theory provide the tools in order to fuzzify abstract notions so that they can be modelled and inferenced in a humanist manner, such that they can be understood by a larger population.
The module will provide a comprehensive introduction to fuzzy logic in addition to the following:
• The concepts of uncertainty, vagueness and imprecision
• Set theory and the notion of a fuzzy set
• Basic operations on fuzzy sets; intersection; union; complement
• Fuzzy inference systems; Mamdani, TSK, zero-order, first-order
• Type-2 fuzzy logic; interval type-2 fuzzy logic; generalised type-2 fuzzy logic
• Fuzzy logic applications
• The use of MATLAB for creating fuzzy inference systems
• Ethical considerations when considering cognitive subjective modelling
• Forwards chaining inference; backwards chaining inference
• Knowledge acquisition
• Knowledge representation
Lecture: 13 hours
Practical: 52 hours
Self-directed study: 19 hours
Assessment: 216 hours
Entry Criteria
ENTRY REQUIRED DOCUMENTS
Home Office Share Code
For EU students only.
IF no Qualification
Please provide CV with at least 2 years of work experience, and employee reference letter.
Entry criteria
Five GCSEs at grade C or above, including English and Mathematics or equivalent, plus one of the following:
Normally 112 UCAS points from at least two A-levels or equivalent or
BTEC National Diploma/ Extended Diploma at DMM or
Pass in the QAA accredited Access to HE. English and Mathematics GCSE required as a separate qualification as equivalency is not accepted within the Access qualification. We will normally require students to have had a break from full-time education before undertaking the Access course or
International Baccalaureate: 26+ points or
T Levels Merit
Portfolio Required: No
Interview Required: No
We welcome applications from mature students with non-standard qualifications and recognise all other equivalent and international qualifications.
UCAS tariff information
Students applying for courses starting in September will be made offers based on the latest UCAS Tariff.
Contextual offer
To make sure you get fair and equal access to higher education, when looking at your application, we consider more than just your grades. So if you are eligible, you may receive a contextual offer. Find our more about contextual offers.
English language
If English is not your first language then an IELTS score of 6.0 overall with a minimum of 5.5 in each component (or equivalent) is essential.
English Language tuition, delivered by our British Council accredited Centre for English Language Learning, is available both before and throughout the course if you need it.
Assessment
ASSESSMENT METHODS
1. INTERNAL ENGLISH TEST if you don't have an English accredited certificate
2. Academic Interview Teaching and Assessments
On this course, you will benefit from Education 2030 - DMU’s new way of delivering courses. Through block teaching, you will focus on one subject at a time instead of several at once.
Key skills, including undertaking research, report writing, presentation skills and essay writing, will be, at least, taught in Year 1 (Level 4), and developed and practised at Year 2 & Year 3 (Level 5 & Level 6).
You may be taught through a combination of lectures, tutorials, seminars, group work, laboratory sessions, practical sessions and self-directed study. Assessment and how assessments are weighted is varied across modules. Our assessment practices reflect the best practices in teaching methods deployed by academic members of staff each year. Indicative assessment weighting and assessment type per module are shown as part of the module information. Again, these are based on the current academic session and are subject to change.
Career Opportunities
Graduate Careers
This course will equip you to work in artificial intelligence in both the public and private sectors, in areas such as market intelligence, imaging techniques, data mining and in the medical and pharmaceutical industries. Graduates wishing to specialise in robotics are well placed to pursue careers in mobile communications and gaming systems.
Our graduates go on to work for major companies such as IBM and Bullhorn Inc.
You will also be well positioned to continue your academic career by embarking on specialised postgraduate study, in either research or taught areas.
DMU Global
Our innovative international experience programme DMU Global aims to enrich studies, broaden cultural horizons and develop key skills valued by employers.
Through DMU Global, we offer an exciting mix of overseas, on-campus and online international experiences, including the opportunity to study or work abroad for up to a year.
Students on related courses have taken part in cyber challenges at the Spyscape Museum in New York and networked with tech entrepreneurs in Silicon Valley.
Placements
During this course you will have the option to complete a placement year, an invaluable opportunity to put the skills developed during your degree into practice. This insight into the professional world will build on your knowledge in a real-world setting, preparing you to progress onto your chosen career.
Artificial Intelligence students have secured placements with leading companies and organisations such as Microsoft and the Defence Science and Technology Laboratory for the Ministry of Defence.
Our Careers Team can help to hone your professional skills with mock interviews and practice aptitude tests, and an assigned personal tutor will support you throughout your placement.
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