The course introduces the AI algorithms suitable for real life challenges in AS: target detection, identification, recognition and tracking, including efficiency assessment and uncertainty reduction.

Artificial Intelligence enables transformation from the remotely controlled autonomous system into the Intelligent Autonomous System. Knowledge of the AI algorithms and ability to implement them effectively in the modern context of autonomous system is a crucial milestone on the way of this transformation.

At a glance

Course structure

The course duration is 5 days; it has 28 hours of teaching time, from which 13 hours are related to practical work; the rest are lectures. Lectures are 9.00 鈥� 16.00 each day apart from the last day finishes at lunchtime.

What you will learn

The course gives a theoretical and methodological background for implementation of Artificial Intelligence into autonomous systems, with the possibility to apply the gained knowledge to real world tasks.

On successful completion you should be able to:

  • Categorize AI methods for real-life scenarios of AS applications.
  • Assess applicability of Artificial Intelligence (AI) algorithms for AS.
  • Set up the commonly used AI algorithms for application in the AS context.
  • Evaluate performance of AI algorithms for a typical AS application in a simulation environment.

Core content

Topics covered by the course include:

  • Introduction to AI for AS with overview of AS sensors and imaging
  • AI Algorithms: Supervised Learning – support vector machine, neural networks and deep neural networks
  • AI Algorithms: Unsupervised Learning
  • Automated Reasoning: Bayesian Networks
  • Lab sessions on Unsupervised and Supervised Learning, including deep learning
  • Case study

Who should attend

This course is the best choice for those who are involved in the design and validation of the autonomous systems, such as unmanned aerial vehicles (UAV) or unmanned ground vehicles. It is also suitable for those that work in areas related to implementation of the Artificial Intelligence for various aspects of their operation.

Speakers

Dr Ivan Petrunin
Dr Luca Zanotti Fragonara

Concessions

20% discount for Cranfield Alumni. 
10% discount when registering 3 or more delegates, from the same organisation at the same time.  

Accommodation fees are not included in the discount scheme. Please ask about our discount scheme at time of booking.

Accommodation options and prices

If you would like to book accommodation on campus, please contact Mitchell Hall or Cranfield Management Development Centre directly. Further information about our on campus accommodation can be found .  Alternatively you may wish to make your own arrangements at a nearby hotel. 

Location and travel

SWAG合集 is situated in Bedfordshire close to the border with Buckinghamshire. The University is located almost midway between the towns of Bedford and Milton Keynes and is conveniently situated between junctions 13 and 14 of the M1.

London Luton, Stansted and Heathrow airports are 30, 90 and 90 minutes respectively by car, offering superb connections to and from just about anywhere in the world. 

For further location and travel details

Location address

SWAG合集
College Road
Cranfield
Bedford
MK43 0AL

How to apply

To apply for this course please use the online application form.

Apply now

Read our Professional development (CPD) booking conditions.