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 in 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.


  • 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.

Increasingly, governments are outlining AI and data revolutions as areas of priority to ensure global leadership for the industries of the future. By embedding AI, government agendas aspire to create new, high-quality jobs and drive economic growth.

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 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 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 participation in: 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.

Some recent projects include:

  • Creating Explainable Interfaces for Autonomous Flight.
  • Mapping Illegal Pollution in Cities using Drones with Electronic Nose.
  • Causal Curiosity using Robot with Digital Mind.
  • Dynamic Contention Pricing in Future Airspace Management.
  • Violence Detection at Airports.
  • Enhancing Airport Safety Using Crowd Monitoring and Social Distancing Analysis.
  • Detection of Vital Signs of Myocardial Infarction Using Computer Vision and Edge AI.

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.

Some recent projects include:

  • Causal Learning in Unmanned/Autonomous Vehicle Dynamic.
  • Eyes-Out Airborne object detection module improving pilots Situational Awareness.
  • Light-weight Deep Learning Approaches for Early Detection of Wildfire and Smoke for UAV Applications.
  • Cipher Key Generation Using Graph Layer Security and Federated Multi-Agent Deep Reinforcement Learning.
  • Generation of Realistic Images From 3D Simulated Environment using conditional GAN.
  • Deep Autoencoders for Unsupervised Anomaly Detection in Wildfire Prediction.
  • Large Language Models for Drone Navigation.
  • Autonomous systems applications of Large Language models.
  • Generative Modelling for Improved Situational Awareness.
  • Drone camera and digital map alignment using an Augmented Reality.


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

    This module aims to equip you 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 decision making 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.
     Introduction to statistical learning
    Statistics fundamentals: probability, random variables, description statistics and stochastic processes
    Statistical inference: estimation and testing, evaluation metrics
     Bayesian methods: Naive Bayes and Bayesian Networks
     Markov processes and chains, Kalman estimators
    Statistical modelling and decision making: regression, mixture models and classification approaches
    Case study: application of statistical learning for aerospace sector problem.
Intended learning outcomes On successful completion of this module you 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

    This module aims to enable you to apply the principles of Systems Engineering to system problems.

    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 you 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

    The aim of this module is to enable you to think critically about technology, solutions, and gain best practices of intelligent systems issues relating to the cyber-physical systems.
    • Cyber-physical systems: Control, sensor and actuators
    • Intelligent agent and multi-agent 
    • Intelligent robotics  
    • Embedded systems 
    • Connected system 
    • Countermeasures. 
Intended learning outcomes

On successful completion of this module you should be able to: 

  • Appraise the theoretical and practical aspects for intelligent system in cyber-physical systems approach.
  • Distinguish the fundamental aspect of intelligent agent, robotics, multi agent systems. 
  • Create working knowledge in dependable control, and embedded systems. 
  • Assess key issues of connected system within the physical world.
  • Analyse different approaches of cyber-physical system with consideration of countermeasures.

Search and Optimisation

    The module aims at giving you 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, non-linear techniques will also be addressed, including multi-criteria methods. 
    • Introduction to Optimisation and Decision Theory  
    • Optimisation models and methods  
    • Integer and Mixed-integer programming 
    • Linear and non-linear programming (including intro to Meta-heuristics) 
    • Decision analysis  
    • Multiple-criteria decision analysis.
Intended learning outcomes

On successful completion of this module you should be able to:

  1. Formulate decision problems based on optimisation scenarios, identifying the different variables. 
  2. Differentiate and apply different optimisation models and methods. 
  3. Evaluate optimisation 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

    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.
    • Introduction to logical representation and reasoning
    • Logical Agents
    • Propositional Logic
    • First-order Logic
    • Inference Algorithms
    • 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 different problem instances using the appropriate formal language to identify relevant knowledge

2.    Building essential but complete Knowledge Bases representing a given knowledge, using Propositional and First Order Logics as representation languages.

3.    Design strategies to enable automated solving of assigned reasoning problems, detailing the execution steps to perform the given tasks.

Data Analytics and Visualisation

    This module will introduce you to data analytics, overview challenges and solutions in this area, present approaches to predictive and descriptive data mining and explain unsupervised learning techniques suitable for new information discovery. Visualisation tools and performance metrics are also considered within the module. You may benefit from knowledge of basic concepts of statistics for performance assessment and evaluation. 
    • Introduction to Data Analytics
    • Data exploration and pre-processing
    • Predictive analytics: regression and classification methods
    • Clustering and dimensionality reduction
    • Graph analysis and visualisation
    • Software and tools for data analytics
    • Case study: application of data analytics techniques and visualisation tools for knowledge discovery problem.
Intended learning outcomes

On successful completion of this module you should be able to:
1. Distinguish stages of the data analytics workflow
2. Categorize data analysis and visualisation 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

    The aim of this module is to introduce you 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.
    • Artificial Neural Networks (Shallow models)
    • Backpropagation and Training
    • Deep learning architectures
    • Convolutional Neural Networks
    • Recurrent neural networks
    • Deep learning applications: object detection, identification, classification, tracking, prediction
    • Introduction to Reinforcement learning
    • Tensorflow practical sessions on Artificial, Convolutional and Recurrent Neural Networks. 
Intended learning outcomes

On successful completion of this module you 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

    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 you 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 you with an appreciation of how to optimise AI integration and the ability to apply practical methods to do so. 

    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 you 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.


This degree has been accredited by British Computer Society (BCS), The Chartered Institute for IT for the purposes of partially meeting the academic requirement for registration as a Chartered IT Professional. Accreditation is a mark of assurance that the degree meets the standards set by BCS. An accredited degree entitles you to professional membership of BCS, which is an important part of the criteria for achieving Chartered IT Professional (CITP) status through the Institute. Some employers recruit preferentially from accredited degrees, and an accredited degree is likely to be recognised by other countries that are signatories to international accords.

Your career

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 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

Organisations that have employed graduates of this course include:

  • Airbus
  • Apifon
  • BAE Systems
  • Frazer-Nash Consultancy
  • Nissan Technical Center Europe
  • Rolls-Royce

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

Cranfield’s Career Service is dedicated to helping you meet your career aspirations. You will have access to career coaching and advice, CV development, interview practice, access to hundreds of available jobs via our Symplicity platform and opportunities to meet recruiting employers at our careers fairs. Our strong reputation and links with potential employers provide you with outstanding opportunities to secure interesting jobs and develop successful careers. Support continues after graduation and as a Cranfield alumnus, you have free life-long access to a range of career resources to help you continue your education and enhance your career.

Part-time route

We welcome students looking to enhance their career prospects whilst continuing in full-time employment. The part-time study option that we offer is designed to provide a manageable balance that allows you to continue employment with minimal disruption whilst also benefiting from the full breadth of learning opportunities and facilities available to all students. The University is very well located for visiting part-time students from all over the world and offers a range of library and support facilities to support your studies.

As a part-time student you will be required to attend teaching on campus in one-week blocks, for a total of 8 blocks over the 2-3 year period that you are with us. Teaching blocks are typically run during the period from October to March, followed by independent study and project work where contact with your supervisors and cohort can take place in person or online.

Normally part-time students are recommended to complete 4 modules in the first year (suggested modules are: Systems Engineering, Intelligent and Cyber Physical Systems, Logic and Automated Reasoning and Ethical, Regulatory and Social Aspects of AI) and 4 modules (suggested modules are: Statistical Learning Methods, Search and Optimisation, Data Analytics and Visualisation, Deep Learning) plus the Group Design Project/Dissertation in the second year. In the case of part-time students, the Group Design Project can be replaced by an Individual Dissertation (from April to August) during the second year. The final year is expected to be focused on the Individual Research Project.

We believe that this setup allows you to personally and professionally manage your time between work, study and family commitments, whilst also working towards achieving a Master's degree.

How to apply

Click on the ‘Apply now’ button below to start your online application.

See our Application guide for information on our application process and entry requirements.

The Applied Artificial Intelligence course at Cranfield University has been a transformative journey for me. Coming from a background as a Data Scientist and Software Engineer, I sought a deeper understanding of AI's mathematical foundations and practical applications. This program not only demystified the complex algorithms behind AI but also provided a unique opportunity to apply these insights within the aerospace domain. It has been instrumental in equipping me with the skills necessary to excel in my role at ANRA Technologies, where I now contribute as a Software Engineer and an AI subject matter expert in UTM, U-Space services, and automation. The course has truly enabled me to leverage digital transformation technologies, such as AI and to be a technology enabler in my field.
The course at Cranfield nurtured my existing strengths and domain expertise while building my skills in Artificial Intelligence Applications in the industry. The highlight of the course was the opportunity to work with industry partners during my thesis, allowing me to apply the knowledge I learnt in the year. I feel the environment at Cranfield really allowed me to develop my potential and prepare myself for the industry. Thanks to my course I am currently working at Airbus in the department of Flight Physics as we develop the future of aerospace.