Please note: a timetable will be sent out to all participants one week before the event. 

In this 1-day course we will look at advanced predictive models, including some machine-learning methods such as neural networks. The idea of this module is to give attendees a useful toolbox of methods they can apply to their own prediction challenges. By the end of the session, the student should be able to implement a range of models using R packages and have a basic understanding of how they work, alongside their pros and cons.

The course will consist of three short lectures and lab sessions (which will involve coding in R). Ideally attendees will have RStudio (www.rstudio.com) installed on their own laptops. 

Note: The course builds on some material surrounding cross-validation from the previous session “Introduction to Statistical Learning (Regression and Variable Selection)”. Material for the previous session is available on request. 

A brief list of topics is given below:

• Introduction to Neural Networks (with implementation via keras)

• Regression and classification trees

• Ensemble Models (random forest)

• Boosting and Bagging predictive models

If you have questions regarding the course material, please email Alex Gibberd (a.gibberd@lancaster.ac.uk)

Important please note:

We will make every attempt to accommodate Lancaster University staff and postgraduate research students on our courses. However, if a course becomes fully booked we reserve the right to give priority to students on the MSc in Statistics, MSc in Data Science, and external participants.

Details of course fees.

Payment: Once you have registered, please pay at the online shop 

https://online-payments.lancaster-university.co.uk/product-catalogue/courses/mathematics-and-statistics/short-courses-and-cpd/applied-statistics-psc-short-courses-20192020

Accommodation Details: Can be found here.

Cancellation Policy

Registrations are transferable to another course or individual at any time. Full refunds will be given for cancellation 10 or more working days before the course start date. Otherwise the full course fee will be charged.