Statistical Learning Methods

Course structure

This five day course is presented through a mixture of lectures, tutorials and discussion of case studies. Active participation from the delegates is strongly encouraged particularly during the whole course in order to consolidate learning. All delegates will receive a Certificate of Attendance upon completion of this course.

What you will learn

On completion of the course, you will be able to:

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

Core content

The course material includes the following:

• Introduction to statistical learning.
• Statistics fundamentals: probability, random variables, descriptive statistics and stochastic processes.
• Statistical inference: estimation and testing, evaluation metrics.
• Bayesian methods: Naïve 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.

Who should attend

The course is of benefit to project engineers, software engineers and analysts who require understanding of statistical backgrounds for autonomous systems, artificial intelligence, decision making and control.

Speakers

The course is presented through lectures and tutorials conducted by members of Cranfield University’s staff all of whom have considerable academic and industrial experience.

• Dr Ivan Petrunin
• Dr Adolfo Perrusquia
• Dr Zhangjia Xu
• Dr Manish Nair
• Dr Cheng Huang

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. Alternatively you may wish to make your own arrangements at a nearby hotel.

Location and travel

Cranfield University 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

Cranfield University
College Road
Cranfield
Bedford 
MK43 0AL