Event Details

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

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

Introduction to Statistical Learning (Exploratory Data Analysis)

This 1-day course explores tools and methods to explore complex data. In particular this session focusses on how we may visualise high-dimensional (i.e. measurements of lots of variables) in low-dimensional spaces via projection. The material is presented with a focus on intuition rather than mathematical details. We will explore various ways to reduce the dimensionality of data, and then perform clustering of the data to identify distinct sets of data-points which share similar characteristics.

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.

A brief overview of topics covered is given below:

• • Basis vectors and linear transforms
  • Principle component analysis (PCA)
  • Independent Component Analysis (ICA)
  • Visualising High-Dimensional data
  • Distance Based Clustering (K-Means, K-Medoids, Partitioning around Medoids)
  • Hierarchical Clustering

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.