Machine Learning faces with increasing availability of large datasets are significant drivers for technological development of robots and autonomous systems, and they are being increasing utilised in new products and services.

The aim of this module is to provide delegates with the necessary knowledge and understanding for the application of machine learning & artificial intelligence techniques to real world industrial problems within the domain of robotics and beyond.

Companies using machine learning to optimize business processes and decision-making have distinct advantages over those that aren鈥檛. By wielding machine learning technology to make business objectives more predictive and prescriptive, data-driven enterprises are redefining how to create and measure value. With the development of free, open-source machine learning and artificial intelligence tools, it鈥檚 never been easier for companies of all sizes to harness the power of data.

Effectively integrating machine learning applications into your business requires a practical understanding of its models. The Machine Learning short course from aims to equip your workforce with the skills to understand the impact of machine learning and apply its models. Your employees will learn to analyze the models and techniques.

At a glance

  • Dates
    • Please enquire for course dates
  • Duration5 days
  • LocationCranfield campus
  • Cost拢1500 Concessions available

Course structure

Working group activities including presentation on the last day. Lectures and computer labs

What you will learn

On successful completion delegates should be able to:

  1. Construct a wide range of machine learning techniques to solve industry problems particularly within the domain of robotics.
  2. Appraise the application of machine learning approaches to a wider set of data mining and classification type problems.
  3. Using a provided implementation, plan machine learning analysis on suitable forms of computer and robotics data.
  4. Examine the concepts and operation of a range of machine learning algorithms in order to facilitate re-implementation in a software programming environment with which they are already familiar.
  5. Develop programme in solving machine learning problems through interactive learning workshops.

Core content

  • Introduction to Machine Learning Theory Applications.
  • Decision tree modelling, logical reasoning.
  • Probability theory and Bayesian methods.
  • Classification methods and clustering techniques.
  • Bio-inspired artificial intelligence algorithms.
  • Reinforcement learning.
  • Case study for robotics applications.

Who should attend

This course is suitable for:

  • Engineers

Speakers

Dr Seemal Asif

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

This course is non-residential. 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 here. 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.

Read our Professional development (CPD) booking conditions.