Artificial Intelligence technologies are being increasingly adopted across a broad range of industries, creating demand for talented graduates who can help realise the transformative potential of AI.  With a fundamental interest is AI, machine vision and computer sciences you will have the desire to apply this knowledge to solve real world engineering problems.

Taught through a unique combination of theoretical and practical based sessions you will cover subjects in logic and reasoning, data analytics, deep learning, agent architectures, alongside the broader systems engineering and ethical considerations required for implementation in real-world systems.

Overview

  • Start dateOctober
  • DurationOne year full-time, two-three years part-time
  • DeliveryTaught modules 40%, Group project 20%, Individual research project 40%
  • QualificationMSc
  • Study typeFull-time / Part-time
  • CampusCranfield campus


Who is it for?

This course provides engineering, physics, computing or mathematics graduates with the advanced skills which can be applied to the security, defence, marine, environmental and aerospace industries. This course is also available on a part-time basis enabling you to combine studying with full-time employment.

Why this course?

Artificial intelligence (AI) and machine learning are redefining the way we live and work, allowing us to automate processes and enhance productivity. These new technologies create the need for skilled engineers with an understanding of their applications and intricacies.

In its Industrial Strategy, the UK government has outlined AI and data revolution as one of its four Grand Challenges, to ensure the UK leads the way for the industries of the future. By embedding AI across the UK, the government aspires to create thousands of high-quality jobs and to drive economic growth. The UK is already a world-leader in AI and at Cranfield we want to continue to bolster this upward trend.

Undertaking this course will allow you to be at the forefront of this ongoing technological revolution, equipping you with both the theoretical and practical knowledge to work across disciplines and implement AI systems where they are needed.

We are well-located for visiting part-time students from all over the world, and offer a range of library and support facilities to support your studies, while balancing work/life commitments. Our MSc programmes benefit from a broad cultural diversity of students, which significantly enhances the learning experience for both staff and students.

Informed by Industry

The course is directed by an industrial advisory panel who meet twice a year to ensure that it provides the right mix of hands-on skills and up-to-date knowledge suitable for to the wide variety of applications that this field addresses. The panel will also propose industry relevant research challenges that will shape the topics for individual thesis projects.

A number of members also attend the annual student thesis presentations which take place at the end of July, a month or so before the end of the course. This provides a good opportunity for students to meet key employers

Course details

The course will include eight taught compulsory modules, which are generally delivered from October to March.

Students are also supported in their learning and personal development through exposure to; industry seminars, group poster session, group discussions, group presentations, video demonstrations, case studies, laboratory experiments, coursework and project work. Students will receive hands-on experience accessing equipment and facilities within both our Aerospace Integration Research Centre and Intelligent Mobility Engineering Centre.

The new MSc in Applied Artificial Intelligence will use standard teaching and assessment methods as well as technology enhanced teaching (TET) methods such as a Virtual Learning Environment (VLE) to support different learning styles. Theories and fundamental of AI will be taught in both lecture and workshop formats where videos and technology demonstrators will be used as teaching aids. Lecture videos will be available on VLE to provide an interactive learning experience.

Course delivery

Taught modules 40%, Group project 20%, Individual research project 40%

Group project

The aim of the group design project (GDP) is for students to design, implement, validate and test an AI-based system, showing an understanding to apply the knowledge acquired in the taught modules and integrate the various concepts. The GDP will provide invaluable experience of delivering a project within an industry structured team, developing transferable skills including; team working with members with diverse backgrounds and expertise, project management and technical presentations.

Part-time students are encouraged to participate in a group project as it provides a wealth of learning opportunities. However, an option of an individual dissertation is available if agreed with the Course Director.


Individual project

The individual research project allows you to delve deeper into an area of specific interest. It is very common for industrial partners to put forward real world problems or areas of development as potential research thesis topics. For part-time students it is common that their research thesis is undertaken in collaboration with their place of work.

Modules

Keeping our courses up-to-date and current requires constant innovation and change. The modules we offer reflect the needs of business and industry and the research interests of our staff and, as a result, may change or be withdrawn due to research developments, legislation changes or for a variety of other reasons. Changes may also be designed to improve the student learning experience or to respond to feedback from students, external examiners, accreditation bodies and industrial advisory panels.

To give you a taster, we have listed the compulsory and elective (where applicable) modules which are currently affiliated with this course. All modules are indicative only, and may be subject to change for your year of entry.


Course modules

Compulsory modules
All the modules in the following list need to be taken as part of this course

Statistical Learning Methods

Module Leader
  • Dr Ivan Petrunin
Aim
    This module aims to equip students with practical knowledge in statistics required for assessment and quantification of uncertainties in real life scenarios of data analysis. Module presents practically important algorithms of statistical learning for both prediction and classification purposes and provides the opportunities for their experimental evaluation during the lab sessions. Tools for evaluation of learning algorithms’ performance are also considered and implemented to practical examples.
Syllabus
    Overview of probability
    Random variables and descriptive statistics
    Statistical Inference: estimation (point and interval) and hypothesis testing (inc. goodness of fit tests)
    Stochastic processes, Markov processes and chains
    Bayesian methods
    Regression methods: linear regression
    Classification: kNN, Naïve Bayes, Discriminant analysis, Decision trees
    Case study: application of statistical learning for aerospace sector problem.
Intended learning outcomes On successful completion of this module a student should be able to:

1. Relate statistical techniques for uncertainty quantification to real life problems.
2. Differentiate experimental data according to the underlying models of stochastic processes.
3. Propose statistical learning methods suitable for particular problem.
4. Assess the outcomes of the statistical learning.

Systems Engineering

Module Leader
  • Tim Mackley
Aim
    This module aims to enable students to apply the principles of Systems Engineering to system problems.
Syllabus

    Topics covered by the course include:

    • Systems challenges
    • The systems process
    • Understanding systems
    • Capability need and requirements
    • System design and architecture
    • System evaluation, verification and validation
    • The impact of organisation on Systems Engineering
    • People, skills and competencies in Systems Engineering  
Intended learning outcomes On successful completion of this module a student should be able to:
1. Illustrate common deficiencies in system design using case studies.
2. Formulate approaches to system design based upon capability, behavioural principles and architectural considerations.
3. Collect comprehensive system requirements from stakeholders formulate their flow-down into the systems design 
4. Design verification strategies for proving the systems design
5. Organise projects to achieve a successful systems design.

Intelligent Cyber Physical Systems

Module Leader
  • Dr Saba Al-Rubaye
Aim
    The aim of this module is to enable students to think critically about technology, solutions, and gain best practices of intelligent systems issues relating to the agent architecture for reliable cyber-physical systems.
Syllabus
    • Cyber-physical systems
    • Intelligent agent 
    • Intelligent multi-agent 
    • Agent architecture 
    • Real-time embedded systems
    • Intelligent robotics 
    • Countermeasures
Intended learning outcomes On successful completion of this module a student should be able to:
1. Appraise the theoretical and practical aspects for intelligent cyber-physical systems.
2. Distinguish the fundamental aspect of intelligent agent architecture and multi agent systems.
3. Create working knowledge in dependable control, sensor and embedded systems.
4. Categorise intrusion detection technique into intelligent cyber-physical systems.

Search and Optimisation

Module Leader
  • Dr Luca Zanotti Fragonara
Aim
    The module aims at giving the students a solid background introduction to optimization and decision theory. The use of modern optimisation methods, especially for applications in artificial intelligence. More than the traditional linear techniques, meta-heuristics will also be addressed, including meta-heuristics inspired in biologic agents.
Syllabus
    • Introduction to Optimisation and Decision Theory
    • Optimisation models and methods
    • Decision analysis
    • Linear programming
    • Meta-heuristics
    • Multiple-criteria decision analysis.
Intended learning outcomes On successful completion of this module a student should be able to:
1) Formulate decision problems based on optimization scenarios, identifying the different variables.
2) Differentiate and apply different optimization models and methods.
3) Evaluate optimization and decision problem criteria.
4) Design decision analysis problems and apply/implement different algorithms and solutions
5) Examine and debate the application of structured approaches to support decision in context of multiple conflicting objectives.

Logic and Automated Reasoning

Module Leader
  • Dr Marta Ceccaroni
Aim
    This module aims at providing the foundations of logic and reasoning modelling to understand how reasoning can be automated. Moreover it introduces the fundamental techniques and formal language to design automated reasoners.
Syllabus
    • Introduction to logical representation and reasoning
    • Logical Agents
    • Propositional Logic
    • First-order Logic
    • Inference
    • Engineering domain knowledge representation
    • Exercises and case studies
Intended learning outcomes

On successful completion of this module a student should be able to:

1.    Analyse syntax and semantics of first-order logic and different knowledge representation using the appropriate formal language.

2.    Use logical programming to build knowledge bases.

3.    Design strategies to solve assigned reasoning problems detailing the execution steps to perform the given tasks.

Data Analytics and Visualisation

Module Leader
  • Dr Ivan Petrunin
Aim
    This module will introduce students to data analytics, overview challenges and solutions in this area, present approaches to data mining and explain unsupervised learning techniques suitable for new information discovery. Visualization tools and performance metrics are also considered within the module. Students may benefit from knowledge of basic concepts of statistics for performance assessment and evaluation.

Syllabus
    Introduction to Data Analytics
    Statistics refresher
    Methods of data pre-processing
    Regression methods
    Dimensionality reduction
    Clustering
    Support Vector Machines
    Visualization
    Case study: application of data pre-processing and machine learning for data analytics in aerospace application.
Intended learning outcomes

On successful completion of this module a student should be able to:
1. Distinguish stages of the data analytics workflow
2. Categorize data analysis and visualization techniques with respect to data analytics stages 
3. Plan data analytics workflow based on the available data and formulated requirements
4. Set up algorithms for discovery of new information from the large data sets
5. Evaluate performance of the algorithms and quality of the data analysis outcomes.


Deep Learning

Module Leader
  • Dr Luca Zanotti Fragonara
Aim
    The aim of this module is to introduce the students to machine learning algorithms, with particular emphasis on supervised learning and deep learning, suitable for real life problems concerning relevant industrial applications from aerospace, manufacturing or transports.
Syllabus
    Classification, Regression, Detection.
    Artificial Neural Networks.
    Deep learning architectures.
    Deep learning training.
    Deep learning applications: target detection, identification, classification, and tracking.
    Adversarial Samples and Generative Adversarial Neural Networks.
    Reinforcement learning.
    Introduction to Tensorflow.
Intended learning outcomes

On successful completion of this module a student should be able to:

1.    Explain fundamental meaning and discuss applicability of machine learning algorithms for industrial applications.

2.    Test the commonly used AI algorithms and describe their applications.

3.    Implement AI algorithms, estimate their performance in a simulation environment and assess their performance for a realistic case study.

4.    Judge AI implementation platforms and create deep learning applications for specific problems.

Ethical, Regulatory and Social Aspects of AI

Module Leader
  • Dr Sarah Fletcher
Aim
    Technology typically advances much faster than society develops an understanding of its potential consequences, and before appropriate regulations and standards are developed to prevent negative impacts. This module aims to provide students with a range of knowledge from different ethical, regulatory and social perspectives that will prepare them for the advancing landscape of AI. It will address fundamental theoretical and practical ethical issues (e.g. data management and security, personal control and consent, biases, etc.) and explore them in relation to current standards and regulatory frameworks. In addition, the module will cover a range of key social science topics and techniques to equip students with an appreciation of how to optimise AI integration and the ability to apply practical methods to do so.
Syllabus

    The sessions in this module will focus on three inter-related strands: ethical, regulatory, and social aspects of AI.

    • Ethical aspects: exploring and debating ethical principles for AI, from fundamental definitions and the development of moral norms and expectations in society to more specific appraisal of how ethics will shape, and be shaped by, the developing landscape of intelligent systems. Key concepts will be appraised and related to context via classroom activities and discussions to predict possible and probable challenges in dealing with ethical behaviours in the design and application of AI and the potential human / social impacts.
    •  Regulatory aspects: examining and evaluating the current state of regulations for AI, how formal standards and guidance specifications are developed, and how they are harmonised and linked to legal frameworks. Existing standards / regulatory documents will be reviewed (subject to availability and access permissions) to facilitate group and individual appraisals of content and usefulness in practice, and to predict potential gaps with respect to the evolving landscape of AI in society today and in the future.
    • Social aspects: identifying and assessing the various current and future social implications of AI, with particular focus on the bi-directional impacts between systems and the surrounding cultural and social environment. Psychological processes and human factors that influence attitudes and behaviours towards intelligent systems and their applications in society will be studied, along with practical human / social data collection, measurement and analysis techniques.
Intended learning outcomes On successful completion of this module a student should be able to:
1. Distinguish key ethical principles and potential impacts of AI on people and society and Interpret relevant regulatory frameworks and standards 
2. Evaluate social and human factors that may influence system design and algorithm bias
3. Evaluate social and human requirements of systems and scenarios
4. Appraise methodologies for the selection of appropriate and unbiased human data collection and analysis across contexts 
5. Estimate the user-centred design principles that need to be applied for optimisation of human-system integration.

Accreditation

Accreditation will be sought for the MSc in Applied Artificial Intelligence from the British Computer Society (BCS) and the Institution of Engineering and Technology (IET).

Your career

The industry-led education makes Cranfield graduates some of the most desirable all over the world for recruitment by both global primes to smaller innovative start-ups looking for the brightest talent.
Graduates from this course will be equipped with the advanced skills which could be applied to the security, defence, marine, environmental and aerospace industries. This approach offers you a wide range of career choices with job roles including:

  • Autonomous systems engineer
  • Machine learning engineer
  • Applied machine learning engineer
  • Data scientist
  • Research scientist
  • Big data engineer

 Others decide to continue their education through PhD studies available within Cranfield University or elsewhere.

How to apply

Online application form. UK students are normally expected to attend an interview and financial support is best discussed at this time. Overseas and EU students may be interviewed by telephone.