A 2 day course: 4th & 5th February 2019

This course develops the concepts of statistical learning  introduced in the first course on Statistical Learning.

It covers both clustering (unsupervised learning) and advanced prediction (supervised learning) methods.

The focus will be on methods which have a statistical interpretation, so model-based clustering through latent class analysis will be covered along with more heuristic methods. Real life examples are used, and there is time for practical use of the methods described using the R and Latent Gold packages.

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-20182019

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.

Topics covered:

Introduction to clustering

Hierarchical and non-hierarchical clustering

Distance metrics and types of linkage

The dendogram

Kmeans

Assessment of clustering performance.

Mixture models

Latent class models.

Longitudinal latent class models.

Neural networks

Other predictive methods